Analytics and data science have seen rapid adoption across all sectors. This month, we decided to find out what students look for when it comes to upskilling or educating themselves in this buzzing field.

Be it the programme construct, brand value or cost — we strive to assess what are the core areas Indian IT professionals and young graduates look for to bridge the learning gap. We have tried to make this survey as comprehensive as possible to get a complete understanding of the roadblocks faced in this sector.

About The Study

We tried to take into consideration the variations in course formats, tools used, work experience of the students and the monetary aspect, in this survey. We took opinions from all those who practice data science — from young professionals to fresh graduates — to get a thorough idea of the upskilling trend in this swiftly-developing area.

Our survey was met with much enthusiasm — and we received 1275 responses. Some of them were expected, and many of them were real eye-openers. So here are criteria which students use to select a data science or analytics course.

What is the most important factor you consider in Data Science or Analytics course?

When students think about a particular Data Science course, they come with a set of preconceived notions regarding the curriculum, brand name, faculty or even peers. Since this is always a tricky choice, we made this into a multiple choice question (where a respondent could select more than one answer). That is why we could really get an idea of what is it that students look for while upskilling:

• The most important factor for most students is course content in a data science programme, with over 26% choosing it over other factors.
• The second most important factor is the kind of projects the course offers their students for a more hands-on experience. Over 18% students rated it as important.

The next key element in a data science course for students is placements — either in the form of jobs or internships — with close to 16% respondents voting in its favour.

Factors by Experience Buckets

What duration of Data Science or Analytics programmes do you prefer?

We divided the question into two separate sections: Short-term vs long-term programme and full time vs part-time programme. Interestingly, most students prefer short-term and part-time programmes.

• Over 66% of our respondents said that they would choose short-term programmes over long-term ones.
• Almost 55% of the students said that they would prefer working with a part-time programme rather than a full-time one.

• Senior professionals look for short term programs, over 78% professionals with 10+ years of experience are looking for short term programs.

• Similarly, Senior professionals look for part time programs, over 84% professionals with 10+ years of experience are looking for part time programs.
• Professionals with less than 5 years of experience are looking for full time program, around 55% say they prefer full time.

What format do you prefer for Data Science or Analytics courses?

A data science or analytics course can be made palatable only when it is presented in an easy-to-learn format. When we asked the respondents about what kind of educational system they preferred for their upskilling course, the response was this:

• Interestingly an overwhelming number of students, over 47%, preferred the mixed, or the hybrid format of education.
• Almost 28% of respondents said that they would like to learn with the help of the online format

And 25% of the students said that they preferred the old-school classroom teaching method for data science and analytics

What kind of content is most palatable for learning?

Most young graduates of professionals who want to upskill use some or the other method of online learning. While many use books, others prefer MOOCs or tutorials. We tried to pinpoint which learning method was the favourite for upskilling. Since this was a multiple choice question, we got a fairly well-distributed set of answers.

• Among them, interactive (hands-on) sessions emerged as the top contender with 19% votes.

Videos and projects were the next favourites with 18% respondents choosing them each as learning methods.

What is the learning curve students aspire for in Data Science or Analytics Certification?

There are many kinds of data science or analytics courses, and they cover a plethora of topics. Some discuss small topics in great detail, and others give a basic knowledge of a variety of topics. Interestingly, here’s what AIM respondents want from their upskilling courses:

• 59% of the respondents said that they the course should cover a large area of topics, and thus have more breadth
• 41% of the students said that the upskilling programme should cover only a handful of topics, but in great detail — more depth

When does industry collaboration come in handy?

Most of the upskilling that takes place in the data science and analytics sector is with the aim of bettering jobs, profiles and opportunities. So when these courses offer industry collaboration, we wanted to know what was the thought behind them, and whether the institutes were on the same page as the students.

• Contrary to popular belief, one of the key things that students look for from industry collaboration is the hands-on experience with projects. That’s why it gained 39% votes.
• 31% of the respondents said that they looked for internship or training opportunities with industry collaboration
• 23% of the respondents were of the opinion that the industry collaboration would offer them the opportunity of learning from working professionals.

Capstone Projects and Placements

Job opportunities and Capstone projects are greatly looked forward to while joining upskilling courses. The results here are therefore very obvious.

• 92% of the respondents said that capstone projects played a very important part while choosing a data science or analytics course
• 57% respondents asserted that internship or placement opportunities are crucial in an upskilling programme.

What is the best time to enrol in a Data Science course?

There have been numerous theories about the right time to enrol in a data science or an analytics course. While some have said that it’s never too late to learn, others have insisted that the earlier the education in New Tech begins, it’s better. Here’s what the respondents had to say:

• A majority of the respondents, 43%, have said that it is better to go for these courses right after graduation or post graduation.
• 33% of the respondents said that gaining some work experience before upskilling is important

How much are students willing to pay for the courses?

Most working professionals and students consider upskilling in data science or analytics an investment. This is because once they gain the knowledge, they have more room to grow and climb up the corporate ladder.

• 35% of the students said that they would not pay more than ₹12,000 for a course in data science or analytics. This price bracket clearly indicates at online programmes.
• Interestingly 18% of the respondents were willing to pay between ₹15,000 to ₹35,000 and ₹40,000 to ₹100,000 each for a course. This showcases that there’s a gap for mid-level, short-term courses in data science and analytics.

Our Respondents’ Profile

The post Survey Results: What Do Students Look For In A Data Science Course? appeared first on Analytics India Magazine.

We recently conducted a survey on what do students look in a data science course, and we got some interesting results. The result suggested that from programme construct, brand value to cost, there are a lot of factors that students have in mind before picking the analytics and data science course.

Based on these findings and other key parameters, we have conducted our annual ranking for full time analytics and data science courses in India. For collecting required data from the institutes, we sent out a form with detailed questions to be filled out by the institutes. The key parameters were course content & comprehensiveness, faculty details such as those with PhD or industry experience, student experience such as post completion engagement. Other parameters included external collaboration, placement assistance and others.

The results are deduced after an extensive research and feedback from students & experts.  The eligibility criteria for the ranking were (1). Programme should be long-term and full-time (duration of more than 5 months), (2). The course should be provided by or in association with a University (Indian or Foreign university).

The form was sent to more than 19 analytics and data science schools, of which 14 responded within a stipulated time. Few of them were rejected because of incomplete data, lack of supporting documents or not fulfilling the eligibility criteria. Each of these parameters have been ranked on the scale of 0-5 where 0 is for worst and 5 for best. The institutes that could not be a part of the list either did not participate in the ranking process or could not make it to our list.

For top 10 analytics and data science executive course, click here.

For top 10 courses on Artificial Intelligence in India, check this.

For top 10 data science training institutes in India, check this.

1. Post​ ​Graduate​ ​Diploma​ ​in​ ​Business Analytics​ (PGDBA) By IIM​ ​Calcutta,​ ​ISI​ ​Kolkata​ ​&​ ​IIT​ ​Kharagpur (Tri-Institute​ ​Course)

Founded: 2015

Mode of delivery: Classroom

Course duration: 24 months (18-Classroom & 6-Internship)

Number of hours: 1750

Cities of operation: Kolkata, Kharagpur

Course fees: ₹ 20 Lacs

PGDBA is a two years full-time residential program offered by ISI Kolkata, IIT Kharagpur and IIM Calcutta. It provides students a lifetime experience of multifaceted learning from not only one but three of the premier institutions of the country. It is the first management institute in India to be accredited by AACSB, AMBA and EQUIS. As a part of the programme, students are imparted training in the statistical, technological and business aspects of analytics.

Parameter 1: Course Content (Rating 4.9 )

Comprehensiveness: The coursework in the first semester at ISI Kolkata includes courses such as probability and stochastic processes, statistical inference, statistical structures in data, computing for data sciences and database management systems. The second semester at IIT Kharagpur includes algorithm design and machine learning, time series and regression. IIM Calcutta focuses on the business application with focus on business economics, financial reporting and analysis, categorical data analysis, business data mining and product management. Additionally, there is a pool of electives for the students to choose from in IIT Kharagpur and IIM Calcutta. It also has a capstone project of more than 3 months.

Student assessment: The students are assessed through both academics and industry exposure. In the academic context, apart from the conventional mid-term and end-term exams, students are required to submit group and individual projects in courses such as financial reporting and analysis, contemporary business analytics, inferential statistics, statistical structures in data, and others. Outside of the course work, students participate in hackathons and coding competitions organised by various corporates and reputed institutions. Students’ performance during the 6-month industry internship in the final semester is evaluated by both the organisation and the three institutes, providing a well-rounded assessment.

Learning resources: The learning resources include books, downloadable resources, LMS, question paper and classroom teaching.

Parameter 2: Faculty (Rating 4.9)

Total no. of faculties: 351

Total no. of faculty members with PhD: 339

Total no. of faculty members with industry experience: 53

Student to faculty ratio: 1.05

Parameter 3: Student Experience (Rating 4.9)

Percentage of students who completed the course: 100%

Post-completion engagement: The alumni engage with students and the management to better equip current students for the industry. Interaction with them through social media besides the frequent guest lectures contributes ​to​ ​industry-specific​ ​learning​. Inputs from the alumni are instrumental in deciding major policy changes and course restructuring.

Placement assistance: They provide full assistance to participants of the programme.

Parameter 4: Other Attributes (Rating 4.9)

Entry criteria for student: Entrance exam

External collaboration: In the orientation week, new students are greeted with talks by top analytics executives at companies like PwC, Aditya Birla Finance and ITC. Organisations such as Western Digital, EY, NPCI and more, also connect to students through workshops, competitions and lectures. It provides 6-month internship as part of the curriculum and is well received by the companies such as Amazon, DEShaw, Johnson & Johnson, Gartner, PwC US Advisory, Barclays, MasterCard, AB InBev.

The overall rating is 4.9.

2. Post Graduate Program in Data Science By Praxis Business School

Founded: 2007

Mode of delivery: Classroom

Course duration: 9 months

Number of hours: 500

Cities of operation: Bengaluru, Kolkata, Tirupati

Course fees: ₹ 5.4 Lacs

Praxis has been playing a pioneering role in creating industry-ready analytics professionals. Praxis was early to recognise the need for trained analytics resources and introduced the first one-year full-time analytics program in the country in 2011 offered in knowledge support from PwC and ICICI Bank. The program has grown in strength in terms of student number and quality, curriculum coverage, relevance and industry acceptance.

Parameter 1: Course Content (Rating 4.9)

Comprehensiveness: The course focuses on tools such as Python, R, SAS, SQL, Hadoop, Spark, MongoDB (NOSQL), Amazon AWS (EMR/ EC2), Tensorflow, QlikView and Tableau; modelling and analytical skills such as statistics, data mining, machine learning, deep learning; communication and visualization skills. It allows applying analytics to business situations in areas such as marketing, finance, retail and others. The capstone project ranges from 1-3 months, while the course is updated every 6 months.

Student assessment: Apart from examinations, a student’s performance is assessed on the basis of preparation of assignments, performances in projects, mid-term tests and surprise quizzes. The institute expects a minimum standard of academic performance (CQPI of 4.00 out of 8.00) for the award of the certificate. Failure to meet the minimum standards of performance would require a student to discontinue the program.

Learning resources: It provides books, downloadable resources, LMS, videos and question papers.

Parameter 2: Faculty (Rating 4.6)

Total no. of faculties: 27

Total no. of faculty members with PhD: 24

Total no. of faculty members with industry experience: 25

Student to faculty ratio: 3.8

Parameter 3: Student Experience (Rating 4.8)

Percentage of students who completed the course: 93%

Post-completion engagement: The students are selected via rigorous methodology to make them able to be placed in good companies and be fluent in new age technologies. The alumni help in providing placement assistance, mentoring, among others.

Placement assistance: They provide full assistance to students, and have been able to provide placements to more than 90 percent of students, batch-on-batch, so far.

Parameter 4: Other Attributes (Rating 4.8)

Entry criteria for student: Only candidates with minimum 60 percent in class X, XII & graduation are eligible. Further, selection is based on entrance exam followed by essay writing and personal interview.

External collaboration: There is a considerable participation of the industry in design and delivery of the analytics program at Praxis Business School. The program is co-created and co-delivered with Knowledge Support from PwC and ICICI Bank. 71% of their faculty members teaching analytics subjects are associated with Analytics organisations.

The overall rating is 4.77.

3. Post Graduate Diploma In Data Science By Manipal ProLearn

Founded: 2016

Mode of delivery: Classroom

Course duration: 11 months (9 months classroom training + 2 months project and Internship)

Number of hours: 800

Cities of operation: Bengaluru

Course fees: ₹ 6.3 Lacs + 18% GST

Manipal ProLearn is a part of Manipal Global Education Services (MaGE), a pioneer in higher education and allied services. The Academy of Data Science with its mission of building a community of industry-ready data scientists, runs the flagship program which is offered in 2 modes – full-time and part-time which aims at upskilling young working professionals in analytics and AI space to help them transform their careers.

Parameter 1: Course Content (Rating 4.8)

Comprehensiveness: The curriculum begins with foundation-level courses including R, Python, data scraping & wrangling, statistics, followed by core courses including machine learning, data visualisation and big data tools, and advanced courses covering AI, deep learning, neural networks and advanced big data. The course also includes hackathons, extra-curricular workshops and sessions on design thinking, soft skills and behavioural skills and others.

Student assessment: There are two main gradable components — internal assessment includes assignments, case studies and quizzes while external includes term and exams. The 2-month capstone project also carries credits. Minimum 75 percent of attendance in each subject is required for the student to be eligible for the term-end exams.

Learning resources: It has learning resources in form of books, downloadable resources, LMS, videos, question papers. The Institute has developed a significant sized repository of data sets and case-studies across subjects which are provided to the students for practicing application of the concepts taught.

Parameter 2: Faculty (Rating 4.4)

Total no. of faculties: 18

Total no. of faculty members with PhD: 5

Total no. of faculty members with industry experience: 12

Student to faculty ratio: 20:1

Parameter 3: Student Experience (Rating 4.7)

Percentage of students who completed the course: 98%

Post-completion engagement: They invite alumni for a talk to share their journey and experiences with newer batches.

Placement assistance: They provide assistance through alumni network and resume designing. They also invite organisation to the campus to screen students for roles in data science and analytics space.

Parameter 4: Other Attributes (Rating 4.8)

Entry criteria for student: Students should be graduate from a recognised institute or university with minimum 50 percent and have maths or programming as a subject in the qualifying degree.

External collaboration: Manipal ProLearn has a strong industry as well as academia connect. Industry collaboration includes those with Equifax, Gramener, whereas academic collaborations are with MAHE and Deakin University (Australia). The students, after successful completion of the PG Diploma in Data Science, are eligible to transition into the Masters of Data Analytics from Deakin. Thus, the student stands a chance to earn a PG Diploma from MAHE and a Masters Degree from Deakin within a span of 2 years.

The overall rating is  4.67.

4. Post Graduate Program In Data Science And Engineering (PGP-DSE) By Great Learning

Founded: 2013

Mode of delivery: Classroom

Course duration: 5 months

Number of hours: 400+

Cities of operation: Mumbai, Bengaluru, Delhi NCR, Chennai, Hyderabad, Pune

Course fees: ₹ 3 Lacs + GST

With a mission to make professionals succeed in the digital economy, Great Learning is offering programs in critical competencies such as analytics, data science, machine learning, AI, cloud computing and more. Their industry relevant programs are helping thousands of professionals who are working across marquee organisations in India and beyond.

Parameter 1: Course Content (Rating 4.7)

Comprehensiveness: The intensive and exhaustive course is designed for early career professionals to acquire relevant skills in data science, which blends right amount of theory and applications. The key areas covered are Python, tableau, regression, classification, decision trees, random forest, SQL and more. The program is in a Boot Camp format, with more than 70 percent of time devoted to hands-on practice, assignments and projects. The capstone project is one month long and course is updated every six months.

Student assessment: The rigorous evaluation mechanism ensures that the quality of the program is maintained and participants achieve desired learning outcomes. Assessments happen in the form of quizz, class presentations, code reviews, projects, lab exercises and exams. Students are also provided personalised feedback to ensure that learning outcomes are met.  

Learning resources: It is provided in the form of books, practice Exercises, lab assignments, downloadable resources, LMS, videos, question paper and mentoring sessions.

Parameter 2: Faculty (Rating 4.8)

Total no. of faculties: 87

Total no. of faculty members with PhD: 31

Total no. of faculty members with industry experience: 87

Student to faculty ratio: 5:1

Parameter 3: Student Experience (Rating 4.6)

Percentage of students who completed the course: 95%

Post-completion engagement: Every graduating student becomes a Great Lakes Alumni. All candidates also receive full placement support for up to three months post program completion. In addition they have access to Great Learning learning material (videos) that is continuously updated. They also get opportunity to mentor students, provide career guidance and others.

Placement assistance: Full placement assistance is provided in the form of feedback on CV, and mock interviews.

Parameter 4: Other Attributes (Rating 4.5 )

Entry criteria for student: Candidates should have scored more than 60 percent across X, XII and graduation, along with a work experience of less than three years. The applicants need to take online test followed by rigorous interview. The final decision of selection is taken by the Program Director.

External collaboration: It has academic collaboration with Great Lakes Institute of Management and industry collaborations with the likes of Microsoft, Accenture, Deloitte, EXL, WNS, Cognizant, American Express, Absolute Data, HSBC, etc. It also has collaborations with Bajaj Allianz, WNS Global, XL Catlin for Ideathons, Hackathons, live capstone projects and customized training programs.

The overall rating is 4.65.

5. MSc In Business And Data Analytics By INSOFE (International School of Engineering)

Founded: 2011

Mode of delivery: Classroom

Course duration: 18 months

Number of hours: 1200+

Cities of operation: Bengaluru, Hyderabad, Pune

Course fees: 17,000 Euros

INSOFE is the reigning leader in the area of data science in India. The curriculum of INSOFE stems from research led by the in-house team of 35+ strong data scientists who work on cutting-edge research and industry leading projects spread across verticals. INSOFE students are abraded with knowledge, skills, tools and techniques to solve complex real world problems, in ways not normally covered by theoretical books.

Parameter 1: Course Content (Rating 4.6)

Comprehensiveness: This is a unique and formidable course seamlessly covers the entire spectrum of business applications. This twinning program enables the students to complete their first semester in India at any of the INSOFE campuses, followed by another semester at Rennes Campus in France. This program intends to metamorphose engineers to face the current wave of advancements in the industry fuelled by data science, analytics and big data engineering. It has a capstone project of 6 – 9 months, with course being updates every 6 months

Student assessment: A multi-pronged assessment approach ensures that learning is maximized. The assessments test the students’ ability to grasp the terminology through concepts and to apply those concepts in business problem scenarios. These are done throughout the program to gauge their progress continuously. There are multiple exams to test the conceptual knowledge and application level understanding of the participants. The projects and assignments have to be completed within stipulated timeframes to be eligible for being awarded grades.

Learning resources: It includes books, downloadable resources, LMS, videos, question papers and various case studies across industries. 

Parameter 2: Faculty (Rating 4.9)

Total no. of faculties: 62

Total no. of faculty members with PhD: 24

Total no. of faculty members with industry experience: 62

Student to faculty ratio: 5:1

Parameter 3: Student Experience (Rating 4.5)

Percentage of students who completed the course: 100%

Post-completion engagement: They provide Visa assistance, in grabbing paid internships, career support through fairs in Europe and India, alumni connect, knowledge sessions through INSOFE LMS and provide up to two years post work Visa.

Placement assistance: They provide full placement assistance through alumni network, resume designing, helping them with interview process, carrying industry talks and others.

Parameter 4: Other Attributes (Rating 4.3)

Entry criteria for student: Entrance exam and interview

External collaboration: In addition to of being Triple Crown Accredited with AACSB – EQUIS – AMBA, Rennes School of Business provides a permanent link between the students, the Faculty members and the French or international companies. They have collaborations with over 100 multinational companies such as Adidas, Coca Cola, Decathalon, Airbus, EY, IKEA, LOREAL, KPMG, P&G and PWC etc.

The overall rating is 4.57.

6. Post Graduation Programme Business Analytics By International Institute of Digital Technologies (IIDT)

Founded: 2016

Mode of delivery: Blended

Course Duration: 1 year

Number of hours: 540

Cities of operation: Tirupati

Course fees: ₹ 5 Lacs

IIDT was initiated by government of Andhra Pradesh under the dynamic leadership of Honorable Chief Minister, Sri. Nara Chandrababu Naidu Garu. It is run under the guidance of J. A. Chowdary, Special Chief Secretary & IT Advisor to the Chief Minister of Andhra Pradesh.

Parameter 1: Course Content (Rating 4.5)

Comprehensiveness: At IIDT, the focus is on the transformational pedagogical approach, which follows a hybrid model. It has a combination of personalised learning and collaborative approaches such as classroom coaching, live projects and industry-driven research. It covers tools such as R, Python, Spark, marketing analytics, among others. The capstone project is of the duration of 3 months, while the course is updated every 6 months.

Student assessment: Course is divided into three trimesters, which is followed by exam and a mini practical project. The final score is given in given in CGPI.

Learning resources: It includes books, downloadable resources, videos, question paper, conference, hackathons

Parameter 2: Faculty (Rating 4.2)

Total no. of faculties: 10

Total no. of faculty members with PhD: 4

Total no. of faculty members with industry experience: 4

Student to faculty ratio: 30

Parameter 3: Student Experience (Rating 4.5)

Percentage of students who completed the course: 100%

Post-completion engagement: It includes alumni engagements in the form of seeking mentorship.

Placement assistance: They provide assistance through alumni network, resume designing and helping them with interview process.

Parameter 4: Other Attributes (Rating 4.3)

Entry criteria for student: Students must take entrance test followed by personal interview.

External collaboration: They have guest lectures from industry network along with ensuring  industry visits for IIDT students. Students can attend conferences, seminars, hackathons with the government and industry connects. Some of the mentor organisations with IIDT are CISCO, Infosys, TCS, Wipro, Fractal Analytics, Wiley, RR Donnelley, SAP, APTS, IBM, eMudhra, HCL, to name a few.

The overall rating is 4.37.

7. Master Of Business Administration (Data Sciences And Data Analytics) By Symbiosis Centre For Information Technology  

Founded: 1999

Mode of delivery: Blended

Course Duration: 2 years residential programme

Number of hours: 1500

Cities of operation: Pune

Course fees: ₹.13.14 Lacs

A constituent of Symbiosis International University, SCIT has been a pioneer in imparting education in niche areas of IT and business management in India for more than a decade. The MBA in data science and data analytics aims at imparting professionals with technology advancement and equipping professionals with AI and data analytics knowledge.

Parameter 1: Course Content (Rating 4.6)

Comprehensiveness: The unique curriculum at SCIT captures end-to-end data life cycle exclusively through hands-on using R & Python (data mining, machine learning, text analytics, data visualization, predictive analytics, deep learning). It also helps in developing and managing infrastructure to support big data analytics — MongoDB, VoltDB, Neo4j, Hadoop Ecosystem, etc. They provide a capstone project for 1-3 months and the course is updated every year.

Student assessment: The evaluation includes continuous and term-end evaluation. Continuous evaluation in general, is formative while the term-end evaluation is summative in nature. Evaluation is done under the supervision and jurisdiction of university, which includes activities such as online test, open book test, research essay, assignments, quizzes, case studies, practical, presentations, viva and others.

Learning resources: It provides books, downloadable resources, LMS, videos, question paper, MOOC courses, open source software tools, usage of softwares like SPSS, AMOS and SAS

Parameter 2: Faculty (Rating 3.8)

Total no. of faculties: 4

Total no. of faculty members with PhD: 4

Total no. of faculty members with industry experience: 2

Student to faculty ratio: 15:1

Parameter 3: Student Experience (Rating 4.4)

Percentage of students who completed the course: 100%

Post-completion engagement: Engagement takes on many forms (academic, employment, research participation, and social activities)

Placement assistance: Full assistance

Parameter 4: Other Attributes (Rating 4.4)

Entry criteria for student: Students are admitted through entrance exam

External collaboration: It has industry collaboration with SAS, SAP, KPMG and others for hackathons and predictive modelling training. It also collaborates with foreign universities for expert lectures.

The overall rating is 4.3.

8. Graduate Certificate In Big Data & Visual Analytics By SP Jain

Founded: 2004

Mode of delivery: Classroom

Course duration: 8 months

Number of hours: 360

Cities of operation: Mumbai, Singapore, Sydney, Dubai

Course fees: ₹ 5 Lacs

S P Jain School of Global Management is an Australian business school offers undergraduate, graduate and courses in business and in data science.

Parameter 1: Course Content (Rating 4.3)

Comprehensiveness: Course includes data science with Python, statistics with R, advanced data structures, Hadoop, Spark, introduction to cloud, SQL, marketing & financial analytics, and more along with internship. The capstone project is of duration 1-3 months with course being updated every 6 months.

Student assessment: The students are continuously assessed through quiz, assignment, homework, test and hackathons.

Learning resources: It includes books, downloadable resources, LMS, videos, question papers.

Parameter 2: Faculty (Rating 4.1)

Total no. of faculties: 12

Total no. of faculty members with PhD: 6

Total no. of faculty members with industry experience: 8

Student to faculty ratio: 4:1

Parameter 3: Student Experience (Rating 4.3)

Percentage of students who completed the course: 90%

Post-completion engagement: The students are placed as data scientists, data analyst and consultants in leading companies. The alumni network helps in mentoring students registered as a part of the programme.

Placement assistance: Full assistance, Resume designing, Helping them with interview process, Professional Grooming

Parameter 4: Other Attributes (Rating 4.1)

Entry criteria for student: STEM background required

The overall rating is 4.20.

9. Post Graduate Diploma in Management (Big Data Analytics)  By Goa Institute Of Management

Founded: 1993

Mode of delivery: Classroom

Course duration: Two year, full time, residential

Number of hours: 1450

Cities of operation: Goa

Course fees: ₹ 15.25 Lacs

GIM is one of the premier B-Schools in its 25th year of existence. It offers full time and part time courses in healthcare management and big data management.  It is a non-profit organisation, operating its own self-contained campus, with best student-faculty ratio in the country.

Parameter 1: Course Content (Rating 4.2)

Comprehensiveness: The course content was evolved after consultation with leading industry practitioners, academicians and consultants in analytics field. Course emphasises hands-on training in data science lab, programming in R, SAS, Spark and others. It is supported by a novel concept of “Industry Associates” who provide live problem and data sets. It also includes capstone project of more than 3 months, while the course is updated every year.

Student assessment: They follow Assurance of Learning (AOL) framework that mandates programme level objectives (PLO) and course level objective (CLO). The typical components include class participation, quizzes, assignments, mid-term exam and end-term exam. Capstone projects and company internships are also taken into consideration. Students failing to get minimum grades at any point during the course have to discontinue the programme.

Learning resources: It includes books, downloadable resources, videos, question paper, live datasets from companies that have signed MoUs with us as Industry Associates.

Parameter 2: Faculty (Rating 4.2)

Total no. of faculties:12

Total no. of faculty members with PhD: 12

Total no. of faculty members with industry experience: 12

Student to faculty ratio: 5

Parameter 3: Student Experience (Rating 4.0)

Percentage of students who completed the course: This is the first batch

Post-completion engagement: We envisage full absorption by our ‘Industry Associate’ companies

Placement assistance: Full assistance through alumni network, resume designing, helping them with interview process.

Parameter 4: Other Attributes (Rating 4.1)

Entry criteria for student: We set CAT/GMAT/XAT/CMAT cut-off, administer our own all-India online test, followed by GD and PI.

External collaboration: They have tied up with SAS institute for pedagogic support in lab courses. They have MoUs with select companies, which would provide students with live problems and data sets. They have signed with HDFC Bank and are in advanced stage of signing with 14 other companies. They are also in talks with some universities abroad such as Santa Clara University and Central European University for collaboration.

The overall rating is 4.12.

10. PGDM-Research and Business Analytics By Welingkar

Founded: 1977

Mode of delivery: Classroom

Course duration: 2 years full time, recognised by AICTE, Govt. of India

Number of hours: 1035

Cities of operation: Mumbai, Bengaluru

Course fees: ₹ 11.6 Lacs

WeSchool is a pioneering management education institution with emphasis on design-led innovative management education. In 2016, WeSchool introduced new 2 year, full time AICTE approved Post Graduate Diploma Programs (PGDM) in ‘Research and Business Analytics’. The institution constantly experiments with new pedagogical approaches to enhance student learning.

Parameter 1: Course Content (Rating 4.2)

Comprehensiveness: The course is 54 percent business domain knowledge and remaining 46 percent is analytics. Analytical tool covers branded softwares like IBM-SPSS, Frontline Analytic solver, Palisade Decision Tools as well as open source software Python, Tableau. Course covers advanced statistics, Data Mining, SQL, Optimisation Techniques, Predictive Analytics, Machine Learning Algorithms, Cognitive Technologies, IOT & Fintech. It also has a capstone project for duration of 1-3 months and course is updated every year.

Student assessment: Students are assessed on the basis of regular internal assignments and term end examinations. Internal assessment has weightage of 40 percent & term end examination has 60 percent respectively. The capstone project is jointly assessed by a team comprising of faculties from academics and industry experts. The hands on capability of various Analytical tools are also evaluated through the internal assignments.

Learning resources: Books, Downloadable Resources, Videos, Question Paper

Parameter 2: Faculty (Rating 3.9)

Total no. of faculties: 6

Total no. of faculty members with PhD: 4

Total no. of faculty members with industry experience: 4

Student to faculty ratio: 1:2

Parameter 3: Student Experience (Rating 4.0)

Percentage of students who completed the course: 100%

Post-completion engagement: Alumni Engagement & mentoring

Placement assistance: Full assistance through alumni network, resume designing and helping them with interview process

Parameter 4: Other Attributes (Rating 4.0)

Entry criteria for student: Entrance exam

External collaboration: They have collaborated with DXC India Pvt Ltd who is a part of global DXC Tech group, which is one of fortune 500 companies giving end-to-end solutions to companies in the fields of banking & capital markets, technology, and more.

The overall rating is 4.0.

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The post Top 10 Full Time Data Science Courses In India- Ranking 2018 appeared first on Analytics India Magazine.

We bring to you the analytics and data science training institute ranking for the year 2018. As a part of the study we reached out to 20+ institutes that are offering training in the area of new tech. We sent out a Google form to the participants that were sorted for eligibility based on these two criteria: 1) They should have multiple programs in analytics and data science, 2) The flagship program not to be a long duration.

The key parameters based on which these training institutes were ranked comprised of course content & comprehensiveness, faculty details such as those with PhD or industry experience, student experience such as post completion engagement. Other parameters included external collaboration, placement assistance and more.

Each of these parameters have been ranked on the scale of 0-5 where 0 is for worst and 5 for best. The institutes that could not be a part of the list either did not participate in the ranking process or could not make it to our list.

See our Top 10 Full Time Data Science Courses In India- Ranking 2018 here.

See our Top 10 Executive Data Science Courses in India – Ranking 2018 here.

See our Top 10 Courses And Training Programs On Artificial Intelligence In India: Ranking 2018 here.

1. Jigsaw Academy

Founded: 2011

Cities of Operation: All over India and from 30+ countries

Flagship Analytics Course: Full Stack Data Science Program

Mode of delivery: Online

Course fees: ₹ 48,400 + GST

Jigsaw Academy has been key in contributing to the evolution of analytics talent in the country. It has produces over 50,000+ data scientists in 30+ countries. They offer training in multiple popular and niche tools such as data science, machine learning, big data and AI technologies. They have a collaboration with the University of Chicago for their programme Post Graduate Program in Data Science & Machine Learning.

Parameter 1: Course Content (Rating 4.8)

Comprehensiveness: The Full Stack programs are aligned to job roles in data science and machine learning, and therefore offer comprehensive coverage of all aspects of data science such as descriptive, predictive, prescriptive with both Statistical and ML algorithms. It also covers tools such as Python, R, SAS, Tableau. The learning methodology emphasises hands-on practice on real life datasets covering multiple domains including Finance, ecommerce, Supply Chain, Healthcare, HR and more. The capstone project duration is 1-3 months whereas frequency of course updation is every 6 months.

Accessing students: The assessment process includes checking conceptual understanding as well as implementation via multiple case study assignments. They are also assessed by capstone project where students have to submit a technical as well as a business solution and go through a viva to explain results.

Learning resources: Mobile app, downloadable resources including presentations, additional reading material, code samples, datasets

Parameter 2: Faculty (Rating 4.3)

Total no. of faculties: 35

Total no. of faculties with PhD: 8

Total no. of faculties with industry experience: 35

Student to faculty ratio: 25:1

Parameter 3: Student Experience (Rating 4.7)

Percentage of students who complete the course: 80%

Post completion engagement: Placement assistance, alumni network, CV review, mock interviews

Placement assistance: They provide full placement assistance.

Parameter 4: Other Determining Factors (Rating 4.8)

Entry criteria of students: No criteria

External collaboration: Jigsaw Academy continuously works with industry Subject Matter Experts and companies to provide datasets for hackathons and contests for students to gain experience certificates, recommendation letters as well as guest lectures on both technical and career related topics.

The overall rating is 4.65

2. AnalytixLabs

Founded: 2011

Cities of Operation: Bengaluru, Delhi NCR, Global reach with our interactive online and video based programs.

Flagship Analytics Course: Data Science Specialization with Machine Learning & AI Deep Learning, Business Analytics 360

Mode of delivery: Blended

Course fees: ₹ 48,000 + taxes 

AnalytixLabs pioneers in high quality training solutions since 2011 and is led by a team of McKinsey, IIM, ISB and IIT alumni with deep industry experience. They offer a wide array of courses for beginners and experienced professionals. With strong clientèle of MNCs and government organizations, they have also successfully delivered programs both in India and globally.

Parameter 1: Course Content (Rating 4.8)

Comprehensiveness: The course is compiled by industry experts from companies such as American Express, BlackRock, Cisco, EXL Services, Genpact, and others. Courses are meticulously mapped to cater to the requirements of various job roles and is divided in sub-modules which are followed with assignments and real-life project work for experiential learning. The trainers and mentors constantly guide students towards the approach they should be adopting when faced problems at work. It also includes capstone project of duration 1-3 months and course is updated frequently every 6 months.

Accessing students: There are three levels of assessments. First is based on case studies, second is internal assessment tools to test conceptual as well as practical knowledge and last of assessment is personal interview sessions with experts which also helps candidates to prepare for job interviews.

Learning resources: LMS

Parameter 2: Faculty (Rating 4.1)

Total no. of faculties: 15

Total no. of faculties with PhD: 1

Total no. of faculties with industry experience: 15

Student to faculty ratio: 40:1

Parameter 3: Student Experience (Rating 4.8)

Percentage of students who complete the course: 80%

Post completion engagement: Provide assistance for profile building, help in interview preparation and mock interviews.

Placement assistance: Full assistance is provided.

Parameter 4: Other Determining Factors (Rating 4.7)

Entry criteria of students: We profile students based on education background, work experience (if any) and suitable course is suggested accordingly. For certain courses STEM background is mandatory.

External collaboration: Their one of the biggest collaborations is with Asean Data Analytics Exchange, Malaysia. They are also in talks with HR ministry directly to impart training on HR analytics.

The overall rating is 4.60

3. INSOFE (International School of Engineering)

Founded: 2011

Cities of Operation: Bengaluru, Hyderabad

Flagship Analytics Course: PGP in Big Data Analytics and Optimisation

Mode of delivery: Classroom

Course fees: ₹ 3.5 Lacs + GST

INSOFE is helping organisations walk the path of AI through its tailored corporate training programs and consulting engagements. Its curriculum is designed such that it is based on the requirements of the industry and inputs are taken from three key stakeholders i.e. the industry, universities and the fellow practitioners.

Parameter 1: Course Content (Rating 4.6)

Comprehensiveness: INSOFE’s course curriculum is specially designed to meet the industry requirements. It is designed such that, it throws light on both conventional methods as well as cutting edge technology. The 23-week intensive program includes sessions starting from the basics of data analytics and big data spread across tools such as visualization, statistical modelling, machine learning, deep learning and other important concepts. The capstone project is of duration 1-3 months and course is updated every 6 months.

Accessing students: INSOFE’s multi-pronged assessment approach ensures that learning is maximised. The assessments test the students’ ability to grasp the terminology through concepts and to apply those concepts in business problem scenarios. These are done throughout the program to gauge their progress continuously. It’s done with the help of “Recall Output Tests (ROTes), Conceptual Understanding Tests (CUTes), Hands-on Lab Tests (HoTs), Mid-term Hackathon (MiTH), Project Hackathon and Defense (PHD), Feedback and Attendance”. Students have to write a project report and defend it.

Learning resources: LMS

Parameter 2: Faculty (Rating 4.9)

Total no. of faculties: 17

Total no. of faculties with PhD: 17

Total no. of faculties with industry experience: 17

Student to faculty ration: 12: 1

Parameter 3: Student Experience (Rating 4.4)

Percentage of students who complete the course: 96%

Post completion engagement: There are alumni meets, alumni knowledge sessions, extended career support, access to recorded video lectures post course completion and guidance for higher education opportunities

Placement assistance: Students are provided full assistance

Parameter 4: Other Determining Factors (Rating 4.4)

Entry criteria of students: Entrance exam

External collaboration: INSOFE has entered into an academic MOU with Rennes School of Business, France, (RSB) which allows graduates of INSOFE’s PGP in Big Data Analytics certification program to progress directly into the 2nd Semester of RSB’s MSc in Data and Business Analytics (full-time) program. The students graduate in France and benefit from all existing visa policies of the French government. The MoU also facilitates faculty from RSB to teach at INSOFE campuses in Hyderabad and Bengaluru.

The overall rating is 4.58.

4. IMS Proschool Pvt Ltd.

Founded: 2010

Cities of Operation: Mumbai, Bengaluru, Delhi NCR, Chennai, Hyderabad, Pune, Kochi

Flagship Analytics Course: Business Analytics and Data Science Course

Mode of delivery: Blended

Course fees: ₹ 50000 to 1 Lac

IMS Proschool is an Initiative of IMS Learning Resources. Proschool in partnership with NSDC – National Skill Development Corporation offers various career oriented and skill enhancement programs including Maharashtra Government’s MSTB approved Postgraduate Diploma in Data Science and NSE Academy Certification course in Business Analytics across 9 cities in India.

Parameter 1: Course Content (Rating 4.5)

Comprehensiveness: The course covers various analytical tools such as R, SAS, Hadoop, Python, Tableau, advanced analytics, text analytics, machine learning, marketing analytics and more. The course includes mentoring from data scientists from companies such as Mckinsey, Deloitte, Mu Sigma, Google, PWC etc. Apart from capstone project which is of duration 1-3 months, there is an access to additional live projects from startups, and workshop on tools such as Azure, Amazon AWS, IBM Watson, Power BI etc.

Accessing students: Before the completion of the course, students have to submit the assignments on all the topics. After the completion of the training program, students have to submit the capstone project & complete the term end exam. Expert faculty evaluates the capstone project through Viva. In the term end exam, students have to solve business problem using tools like R or Python to demonstrate their understanding of the course.

Learning resources: LMS

Parameter 2: Faculty (Rating 4.3)

Total no. of faculties: 50

Total no. of faculties with PhD: 0

Total no. of faculties with industry experience: 50

Student to faculty ratio: 20:1

Parameter 3: Student Experience (Rating 4.5)

Percentage of students who complete the course: 50%

Post completion engagement: Workshop on career guidance, CV building, interview preparation, updates on latest technologies and additional capstone projects.

Placement assistance: Partial assistance

Parameter 4: Other Determining Factors (Rating 4.5)

Entry criteria of students: Industry experience or STEM background required

External collaboration: They have external collaboration with National Skill Development Corporation, an initiative of Govt. of India, NSE academy, an initiative of National Stock Exchange, Praxis School, Pi R Square.

The overall rating is 4.45.

5. UpX Academy

Founded: 2016

Cities of Operation: Hyderabad, Online, hence all cities

Flagship Analytics Course: 6 Month Certificate Programme in Data Science

Mode of delivery: Online

Course fees: ₹ 80,000 

The institute aims to address the challenges in the industry and empower professionals through their transformational courses in Big Data and Data Science. It is helping the tech industry worldwide to address the problem of shortage of skilled workers in the fields of Big Data & Analytics. The course is designed by industry experts which integrated real-world hands-on training with case studies and projects using datasets from companies like Amazon, Barclays, Nike, Yelp, Walmart, Netflix, etc.

Parameter 1: Course Content (Rating 4.6)

Comprehensiveness: It is a one-of-a-kind program- which helps in becoming a Full Stack Data Scientist in a span of 6 months. The course includes advanced concepts such as NLP using Deep Learning, and basic concepts such as Python, Statistics, Machine Learning and Tableau. The capstone project is of duration 1-3 months and the frequency of course updation is every 6 months.

Accessing students: After candidates clear the CASE base examination, they earn a certification from Tech Mahindra. The industry-recognised certification boosts prospects and add value to their CVs.

Learning resources: LMS, e-books, in-house case studies, articles

Parameter 2: Faculty (Rating 4.5)

Total no. of faculties: 29

Total no. of faculties with PhD: 3

Total no. of faculties with industry experience: 29

Student to faculty ratio: 45:1

Parameter 3: Student Experience (Rating 4.5)

Percentage of students who complete the course: 83%

Post completion engagement: Bootcamps, master classes, career mentoring sessions, resume building sessions

Placement assistance: Guiding the candidates for employment, conduct industry immersion sessions, bring people from the industry to interact with students. Whenever organisations have any requirements, they float these job descriptions in student community.

Parameter 4: Other Determining Factors (Rating 4.2)

Entry criteria of students: No criteria

External collaboration: UpX academy is powered by an IT major Tech Mahindra.

The overall rating is 4.44.

6. Edvancer

Founded: 2013

Cities of Operation: Mumbai, Hyderabad, Global through our online courses

Flagship Analytics Course: PG Certification in Data Science & AI

Mode of delivery: Blended

Course fees: ₹ 45,990

Edvancer is one of India’s leading data science training institutes, providing training in analytics, data science, machine learning, AI & big data globally. It has trained over 7,000 professionals in 25 countries in these areas. It is an IIM-IIT-ISI alumni venture for building capabilities in data science & AI and works with over 50 leading corporates including Fortune 100 companies.

Parameter 1: Course Content (Rating 4.3)

Comprehensiveness: The course covers everything from predictive analytics, machine learning, deep learning, big data development and data visualization in tools like R, Python, Tensorflow, Keras, Tableau, Hadoop, Spark and SQL. It has capstone project of duration 1-3 months and course is updated every 6 months.

Accessing students: Project-based assessment: They work on 10 different projects on which they are assessed.

Learning resources: Books, downloadable resources, LMS, videos, question paper, case studies and more.

Parameter 2: Faculty (Rating 4.4)

Total no. of faculties: 25

Total no.of faculties with PhD: 8

Total no. of faculties with industry experience: 25

Student to faculty ratio: 15:1

Parameter 3: Student Experience (Rating 4.3)

Percentage of students who complete the course: 70%

Post completion engagement: Students have lifetime access to course content and faculty support. Consistent engagement through newsletters on industry updates and job postings. Any upgrades in course material are also provided free of cost.

Placement assistance: Placement assistance post the completion of course is provided

Parameter 4: Other Determining Factors (Rating 4.4)

Entry criteria of students: Entrance exam

External collaboration: Edvancer has partnered with Wiley, the world’s largest education provider for a globally recognized certification in Big Data and Hadoop. It has also partnered with corporates like Ernst & Young, PwC, Deloitte, General Mills, Cognizant, HDFC Bank, Kotak Mahindra Bank, Genpact, GE Capital, TVS Credit, Munich Re amongst many others for hiring its students and training their employees and clients.

The overall rating is 4.35.

7. Ivy Professional School

Founded: 2007

Cities of Operation: Mumbai, Bengaluru, Delhi NCR, Chennai, Hyderabad, Kolkata, Pune, Students belonging to 10+ Countries take Ivy’s Online Live classes

Flagship Analytics Course: Data Science with Machine Learning Certification Program

Mode of delivery: Blended

Course fees: ₹ 56,462

Ivy Professional School has been a leader in the data science and analytics training industry since 2007.  More than 15,500+ students and professionals from elite colleges like IITs, IIMs, NITs, and companies like Honeywell, Oracle, Accenture, IBM, eBay/PayPal, GE, KPMG, Deloitte, Moody’s, Infosys, etc. have been trained by Analytics experts from Ivy.

Parameter 1: Course Content (Rating 4.1)

Comprehensiveness:  The course which has been designed with contribution of experts from companies such as Microsoft, Google, Genpact, HSBC covers advanced analytics and machine learning concepts such as Excel/VBA, SQL, SAS, R, Python, Tableau, Scala, Spark, etc. Special focus is laid on experiential learning by including case studies for each topic. Students also go through rigorous career coaching and 1-to-1 mentoring by expert data scientists. The capstone project is of duration more than 3 months and course is updated every 6 months.

Accessing students: Students go through a rigorous process of assessment after every module completed in the course. They are assessed on the basis of test, quizzes, self- exercise contribution, case studies, capstone projects etc. Every students needs to obtain a score of at least 70% overall and 85% in every module to be awarded the Certificate.

Learning resources: Question papers

Parameter 2: Faculty (Rating 4.8)

Total no. of faculties: 60

Total no.of faculties with PhD: 18

Total no. of faculties with industry experience: 60

Student to faculty ratio: 15:1

Parameter 3: Student Experience (Rating 3.9)

Percentage of students who complete the course: 85%

Post completion engagement: They create engaged and supportive alumni network and engage them with life long placement support, resume upgrading sessions, industry projects workshops, seminars, and others.

Placement assistance: They provide full assistance.

Parameter 4: Other Determining Factors (Rating 4.2)

Entry criteria of students: Entrance exam

External collaboration: They have enhanced industry partnership with large firms such as Nielsen, PwC, Honeywell, Genpact, etc. to create exclusive Right2Hire batches where the anchor industry partner becomes knowledge partner and gets the right to hire the candidates. They also have learning partnerships with Fortune 50 large manufacturer, university and college association with IIT, IIM, Xavier’s, IIFT, Delhi University, etc. to train their students on Data Science.

The overall ranking is 4.25.

8. Nikhil Analytics

Founded: 2012

Cities of Operation: Bengaluru

Flagship Analytics Course: Data Science Modelling & Machine Learning

Mode of delivery: Blended

Course fees: ₹ 60,000

Nikhil Analytics was started in 2012 with a focus on providing hands on training in analytics and build quality candidates who can fill the gap of talented resource in this domain. Till date they have trained thousands of candidates and helped them to make their career in analytics and data science.

Parameter 1: Course Content (Rating 4.2)

Comprehensiveness: Their course on analytics, data science, machine learning covers tools such as as MS Excel, SQL, SAS, R, Python, Tableau. The training is completely hands-on where candidates learn the concepts by practising it. The course also covers capstone project of duration 1-3 months and the course is updates every 6 months.

Accessing students: After set of certain topics are covered in a module, test is conducted. There is a practice test followed by hurdle test that has passing percentage. Unless candidates have passed these, they cannot move to the next level. In the end full length tests are conducted and candidates needs to score minimum passing marks to earn a certificate of course completion.            

Learning resources: LMS

Parameter 2: Faculty (Rating 3.7)

Total no. of faculties: 8

Total no. of faculties with PhD: 0

Total no. of faculties with industry experience: 8

Student to faculty ratio: 15:1

Parameter 3: Student Experience (Rating 4.2)

Percentage of students who complete the course: 90%

Post completion engagement: They have engagement programs for candidates completing the course in terms of hiring them as interns, deploying to different analytics companies, assisting them with resume preparation, conducting interviews and helping them to get jobs via different channels such as HR contacts, alumni network, job consultants etc.

Placement assistance: They provide placement full assistance

Parameter 4: Other Determining Factors (Rating 3.9)

Entry criteria of students: STEM background required

External collaboration: In past they have had collaboration with NTU Singapore. For this year also they are in talks with different universities for external collaborations. They had conducted 1-2 day analytics workshops for different colleges such as ISME Bangalore, NIT Jamshedpur, IIM Bodhgaya etc.

The overall ranking is 4.00.

9. Imarticus

Founded: 2012

Cities of operation: Mumbai, Bengaluru, Delhi NCR, Chennai, Hyderabad, Pune

Flagship Analytics Course: Big Data and Machine Learning Prodegree with IBM as the EdTech partner

Mode of delivery: Blended

Course fees: ₹ 1.10 Lacs for classroom training and ₹ 80,000 for online instructor-led training

Imarticus Learning is a professional education firm that offers industry- endorsed trainings in Analytics & Financial Services and empowers individuals and large organisations in meeting their human capital and skill set requirements. It has educated over 30,000 individuals globally with 85 percent of student placed in global firms such as Wipro, Infosys, Cognizant, Accenture amongst others.

Parameter 1: Course Content (Rating 4.0)

Comprehensiveness: The course is designed by IBM and covers topics around multiple and linear regression, deep learning & Neural Networks with tools such as Python, Hadoop, Spark, SciKit-Learn & IBM Watson. It also has hands-on projects to master technologies behind new-age solutions. The capstone project is of duration 1-3 months and course us updated every 6 months.

Accessing students: As a part of in-class project, students are required to work on a project under the trainer’s supervision at the end of each module. Students are accessed after each semester. For capstone project, students have to submit a synopsis of the project followed by the coding. The final project is then presented to a panel of industry experts for assessment.

Learning resources: LMS

Parameter 2: Faculty (Rating 4.52)

Total no. of faculties: 42

Total no. of faculties with PhD: 8

Total no. of faculties with industry experience: 42

Faculty to student ratio: 20:1

Parameter 3: Student Experience (Rating 3.7)

Percentage of students who complete the course: 95%

Post completion engagement: Upskilling workshops, student guest lectures, and alumni meets.

Placement assistance: They provide full assistance

Parameter 4: Other Determining Factors (Rating 3.7)

Entry criteria of students: STEM background required

External collaboration: They have collaborations with leading firms to ensure that the curriculum is timely, relevant and futuristic. The Machine Learning Prodegree is endorsed by IBM. Imarticus Learning has also established alliances with global analytics firms namely, Genpact and DXC Technology.

The overall ranking is 3.98.

10. Inventateq

Founded: 2009

Cities of operation: Bengaluru

Flagship Analytics Course: 4 Month Comprehensive program in Data Science

Mode of delivery: Classroom

Course fees: ₹ 16,000

Inventateq believes that choosing what to study and what course to enrol is one of the biggest challenges and therefore they encourage students to pick right software course by counselling. The institute provides quality and project-based training for thousands of students and support them get their dream job with their placement program.

Parameter 1: Course Content (Rating 4.1)

Comprehensiveness: The in-depth and advanced data analytics course content is designed by industry experts based on current job requirements, real-time data sets provided to students to practice and gain hands-on exposure. The capstone projects are duration of more than three months and course is updated every 6y months.

Accessing Students: The students are accessed for their knowledge and skills through mock exams, mock interviews, and giving them more real-time scenarios to do assignments.

Learning Resources: Books

Parameter 2: Faculty (Rating 3.7)

Total no. of Faculties: 8

Total no. of Faculties with PhD: 0

Total no. of Faculties with Industry Experience: 8

Student to Faculty Ratio: 15:1

Parameter 3: Student Experience (Rating 3.9)

Percentage of students who complete the course: 100%

Post completion engagement: Counselling

Placement assistance: They provide full assistance

Parameter 4: Other Determining Factors (Rating 3.7)

Entry criteria of students: Student should have a professional degree

External Collaboration: They have tie ups with companies for placement programs

The overall rating is 3.85.

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The post Top 10 Data Science Training Institutes In India – Ranking 2018 appeared first on Analytics India Magazine.

A recent study indicates that Indian analytics, data science and big data industry is estimated to be $2.71 billion in revenues and growing at a healthy rate of 33.5% CAGR. This suggests that Indian domestic firms are doing quite well for themselves and is slowly becoming deep-rooted with its presence in various industries. Tapping on to the growing analytics industry, this year’s study on the state of analytics at domestic firms in India aims to bring a quick insight into how the analytics scenario is evolving in the domestic market.

We looked at 50 large Indian firms across industries that have adopted analytics to improve business. These companies are from industries such as auto, private banks, public banks, financial services companies, infrastructure, steel, power, telecommunications, utilities, ecommerce, oil drilling and refineries. This year we collaborated with Insofe, a leading data science education provider, to bring out this study.

Read our last year’s study here.

Analytics penetration is a metric that denotes the degree of infusion of analytics function in an organisation. It quantifies the approximate number of analytics professionals employed by the organisation for every employee within the whole firm. So, a penetration of 1% should be read as 1 analytics or data science professional for every 100 employees with an organisation.

Analytics maturity is the metric to quantify the quality and depth of analytics adopted within an organisation. Maturity is a combination of three factors:

1. Employee tenure
2. Percentage of advanced analytics & data science in the analytics function
3. Employee seniority in that organisation

Analytics adoption is calculated at a penetration of less than 0.75% i.e. an organisation does not even have one analytics professional to support 133 employees within the organisation. Or it can be said that if a company has adopted analytics, then it would have at least one employee to support every 133 employees.

Analytics & Data Science In Indian Firms: Key Trends

• The overall adoption of analytics & data science at large Indian firms is around 64%. That’s a healthy adoption rate given most of these large firms are into traditional businesses like energy & utilities.
• Telecom, financial services, ecommerce & private sector banks have almost 100% adoptions rates. There is some level of analytics & data science being executed in these organisations, especially the large ones.
• Infrastructure sector has almost no adoption of analytics.

• 44% of all analytic functions for Indian firms are based out of Mumbai, followed by Delhi NCR at 31%
• Almost 94% of analytics functions are based out of just three cities – Mumbai, Bangalore & Delhi/NCR
• 44% of analytics functions in large Indian organisations support the sales & marketing group. Whereas 33% support the finance & operations group

Indian Firms Key Metric For Analytics Function

• On an average, large Indian firms have an analytics penetration of 2.8%. This essentially implies that for every 36 employees in an organisation, 1 employee is in some shape and form associated with data and analytics. Analytics penetration is typically higher for new age ecommerce firms and lower for traditional firms in India.
• The average tenure of analytics professionals at Indian firms is 3.4 years.
• Public sector banks, even though with low adoption have the highest analytics tenure among India firms, at almost 5 years.
• On the other hand, ecommerce firms, with high adoption of analytics, have a much lower analytics tenure at 2.5 years.

• The median experience level of analytics professionals with Indian firms is 7.5 years.
• Analytics professionals in Indian telecom firms have the highest experience at 9 years.
• Oil drilling & refineries have the lowest at 5.9 years.

Analytics Penetration Vs. Maturity (PeMa Matrix)

Like the previous year, here we analysed various sectors on the basis of analytics penetrations and analytics maturity.

Following conclusions can be made from the graph:

• Unlike last year, there does not seem to be a correlation between maturity & penetration. Till last year, maturity and penetration were negatively correlated implying that as firms grow in terms of their analytics penetration, they tend to lose on depth and maturity. This might be changing now as firms are getting more mature in deploying their analytics solutions.
• Ecommerce is the only sector to be in the first quadrant, which signifies that it is both high on penetration and maturity. Data science forms the core to ecommerce sector and all mid to large ecommerce firms have some form of analytics adoption within the firm.
• The highest number of sectors lie in quadrant II. This is good indication of adoptions as well as relative maturity of analytics functions within these sectors.
• The analytics build-up for ecommerce is so high that it makes other sectors almost appear in a vertical line (have same penetration).
• Inmobi has the highest penetration of analytics and data science.
• Traditional businesses with large overlap with either government and/or manufacturing are in quadrant III, signifying both low relative maturity and penetration than other sectors. This can be either due to slower adoption, the design of the organisation itself or lack of use cases.

Analytics Penetration Vs. Maturity- Private Sector Banking

• Private sector banking in India score high on maturity of analytics and data science deployed in the organisations, which is slightly lower than telecom and ecommerce industry.
• Finance & operations are the two primary functions where private sector banks deploy analytics. Marketing is the third function where analytics gets deployed.
• Mumbai is the top city where analytics functions of private Indian banks functions.

Analytics Penetration Vs. Maturity- Public Sector Banking

• Public sector banking in India has the lowest analytics penetration among other sectors, and just slightly higher than oil drilling and refineries sector.
• We looked at eight large public-sector banks and just four out of them seem to have some level of adoption of analytics.
• Union Bank of India & IDBI Bank have the largest analytics penetration among their peers.
• Again, Mumbai is the top city where analytics functions of public Indian banks functions.
• Given the focus that GoI has on latest technologies, it would surely be most applicable in the public sector banks in India.

Analytics Penetration Vs. Maturity- Auto

• Auto sector has high adoption rate of analytics and data science. 5 out of 6 auto companies in India have some form of analytics adoption.
• Mahindra & Mahindra have the highest penetration of analytics & data science relative to its peers. It also has the highest maturity.
• Most analytics functions in auto industry supports the sales and business development units.
• Most analytics functions in auto industry are based out of Mumbai, followed by NCR.

Analytics Penetration Vs. Maturity- Ecommerce

• Ecommerce has emerged as one of the largest adopters of analytics and is evident with their focus on hiring senior analytics professionals.
• It is the leading sector in India in terms of both analytics penetration and maturity.
• Inmobi has the highest penetration of analytics & data science compared to its peers. It also has the highest maturity.
• Inmobi is followed by Shopclues in terms of analytics penetration.
• Most analytics functions in ecommerce industry support the marketing and business development units; followed by operations and supply chain.

Analytics Penetration Vs. Maturity- Oil Drilling & Refineries

• We looked at 8 large-sized oil drilling & refinery companies in India. Analytics adoption is dismally low in this sector.
• Just 1 out of 4 large sized companies have some form of analytics adoption. This subsequently goes down further with mid-sized companies.
• Reliance Industry is leader in this space in terms of analytics adoption. Both in terms of penetration and maturity, RIL scores high.

Analytics Penetration Vs. Maturity- Utilities

• We looked at 5 large-sized utilities companies in India. Analytics adoption is high in this sector.
• 4 out of 5 large-sized companies in this sector have some form of analytics adoption.
• Hindustan Unilever (HUL) is leader in this space in terms of analytics adoption. Both in terms of penetration and maturity, HUL scores high.

Conclusion

The study is indicative of the fact the analytics adoption is witnessing a growth in various industries, including both large firms and traditional businesses. In terms of numbers, the overall adoption of analytics and data science in large firms is around 64% which is significantly high compared to last year. However, there are certain industries such as infrastructure that has almost no analytics adoption, and this could be due to lack of resources to set up analytics facility.

Overall, we see ecommerce emerging as one of the largest adopters of analytics and this could be attributed to their focus on hiring senior analytics professionals in their team. In terms of analytics contributing to various functionalities, it was seen that around 44 percent analytics functions support sales and marketing groups, whereas 33% support finance and operations group.

Complete Report

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Data Science is an emerging field which is now being integrated with industries across all sectors. This year Analytics India Magazine, in association with Great Learning, decided to find out what goes on behind the making of a good Data Scientist. We spent a lot of time finding out the tools and techniques used by these new technology professionals.

From language to coding and GPUs, we garnered interesting and insightful answers from our comprehensive survey.

About The Study

The samples were collected by asking respondents to fill in a survey created by AIM about what tools and techniques data scientists use at work. This included various sub-topics such as data visualisation tools, preferred operating systems and programming languages, among others. We took opinions from all those who practice data science — from professionals with less than two years of experience to CXOs — to get a thorough idea of the working environment in this growing field.

Our survey was met with much enthusiasm — and we got some great insights from it. Some of them were expected, and many of them were real eye-openers.

Which Language Do Data Scientists Prefer For Statistical Modelling?

• The favourite language for data scientists in today’s era is Python, as almost 44% of the professionals use it the most
• A close second is R at 35% — another clear favourite with the data scientists, due to its versatility
• SQL (6%) and SAS (7%) claim only a minor share of the attention of the data scientists

Which Data Science Methods Are The Most Popular At Work?

In this section, we asked the data scientists to pick out the most frequently-used statistical method.

• 72% scientists answered that they used Logistic Regression most at work
• This was followed by Decision Trees at 56% and Neural Network at 48%

Which Is The Most Popular Python General Purpose Library?

Python is one of largest programming community in the world. There are plenty of libraries which a data scientist can use to analyse large amounts of data. But here are our readers’ favourites:

• Pandas emerged as a clear choice for most data scientists at almost 41%
• Numpy was the second-favourite at 24%
• Sklearn and MatPlotLib followed at 17% and 14% respectively

Which Tools Do Data Scientists Prefer?

With a plethora of data analytics tools available online, we asked data scientists if they were willing to use open sourced tools at work. The answer was a resounding yes.

• Almost 89% of the data scientists said that they preferred to work with open sourced tools
• Only 8% data scientists said that they liked to work with custom-made tools which are tweaked and personalised for their particular projects

Which Dashboard/Visualisation Tools Do Data Scientists Prefer?

Data visualisation may be a tricky path for many data scientists. Crunching numbers is one thing, but telling a story with numbers is a whole different deal. When we asked about this to our readers they had one clear winner:

• More than half the respondents, 51%, said that they preferred to use Tableau as a dashboard or visualisation tool.

Which Cloud Provider Do Data Scientists Prefer?

Information flow is a part of data science. While data usage and storage are important, security and privacy of the data are also key to the job.

• Amazon Web Services is a clear winner here with over 45% of the votes
• Google Cloud is the second favourite with over almost 34% votes

What Kind Of Learning Resources Do Data Scientists Use To Keep Themselves Updated?

With the ever-changing technology, it is vitally important for data scientists to keep themselves updated. And they seem to have found out an interesting way to do so!

• 76% of our readers said that they liked watching tutorials and videos on YouTube.
• Almost 54% of the data scientists said that they like learning the old-school way — through books and e-books.
• 46% of respondents also look at MOOCs as a way to upskill themselves.

Where Do Data Scientists Find Open Data?

Finding open data is not that hard, but getting clean open data is often a trying experience. No data scientist wants to waste their time cleaning it. There were four clear popular options here:

• 27% respondents use GitHub
• 22% readers used university websites and the data uploaded by them for research
• 20% data scientists also use data publicly uploaded on official government websites
• 15% of the respondents source their data manually

Which OS Do Most Data Scientists Use At Work?

Compatibility with their tools and ease of use are two key factors. For this question, the respondents had a liking for one OS:

• Almost 69% of data scientists use Windows OS
• 24% prefer Linux
• And only 7% prefer MacOS

Preferred Development Environment

An integrated development environment (IDE) is very important to set up and streamline data science processes. Among the many options presented, the data scientists who took part in our survey chose:

• Almost 38% prefer using RStudio
• And close to 37% data scientists like using Notebook

How Is Code Shared At Your Workplace?

Like we said earlier, privacy, operational efficiency and security are of paramount importance in any organisation that deals with data. Here’s what we found out:

• Over 45% of the respondents use Git to share codes at workplaces
• 28% of the data scientists said that their organisations use cloud-based programmes to share codes
• And 24% of our readers shared codes over non-cloud based programmes

What Is The Neural Network Architecture Data Scientists Use Most Frequently?

Neural networks are a crucial part of programming as well as data science. We got a clear picture that the data scientists, as well as their organisations, use a variety of architectures. According to our study, convolutional neural network was the most frequently used NN at 33%.

Which Big Data Tool Have You Used The Most?

From open source tools to paid or customised ones, many professionals prefer different tools based on the projects or the organisation they are working for. Data scientists from our survey rated their most-favoured big data tools in the following order:

• 52% of the users said they used Hadoop the most
• Almost 22% data scientists used NoSQL

Which GPUs Do Data Scientists Use At Work?

Over 19% of our respondents said that they preferred using the NVIDIA GeForce GTX 8 Series for intensive data usage. The GTX 8 series model is a middle-level GPU — multipurpose and flexible.

Our Respondents’ Profile:

Conclusion

As the Analytics industry grows at the rate of 33.5% CAGR, more professionals are expected to segue into the Data Science and Analytics sector. We realised that apart from hard work and dedication, the tools and skillsets also play a key role in the success of data scientists. Some of the eye-opening inferences were that Python is still the all-time favourite programming language preferred in the Analytics and Data Science sector. The most popular Data Visualisation tool used in this industry right now is Tableau. Another interesting aspect that we found was professionals were aware of the importance of upskilling themselves and how willing they were to do so. Most working professionals like to keep themselves updated by watching videos and reading books. Overall, the study reveals a positive picture of the Indian Analytics and Data Science sector

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The post Data Science Skills Study 2018 – By AIM And Great Learning appeared first on Analytics India Magazine.

Everyone is talking about Artificial Intelligence — the new normal, which has entered almost every work process across industries. Enterprises are rethinking and strengthening their AI capabilities, using it as a tool to improve products and services. With AI becoming crucial to enterprise success, upskilling has become the new mantra among Indian IT professionals, who are keen to make an impact in their careers with Machine Learning and AI.

In our annual AI Study with Great Learning, we take a look at key AI trends dominating the Indian AI market-leading companies, professionals, salaries, jobs broken down by cities and how AI’s potential for industry growth has risen over the last few years. In the second half of the study, we cover AI literacy in India through Great Learning’s comprehensive AI/ML programs that are bridging the current skill gap and consequently boosting workforce transitions.

Key Trends

• The Artificial Intelligence Industry in India is currently estimated to be $230 million (annual) in revenue, up from $180M a year ago
• Currently, there are approximately 40,000 AI professionals in India
• There is a growing interest in AI with the Indian government doubling up spends on AI/ML research
• AI applications have emerged in key areas like healthcare and agriculture
• Bengaluru outpaces its peers in terms of attracting AI talent and has emerged as a leading AI hub in India

AI Professionals in India

• The average work experience of AI professionals in India is 6.6 years, same as last year
• Around 3,700 freshers were added to the AI workforce in India this year
• Almost 55% AI professionals in India have a work experience of fewer than 5 years, same as last year
• 23% of AI professionals have more than 10 years of work experience. This work experience is not necessarily in AI but these professionals have transitioned into AI over time
• Women participation in AI workforce remains low – only 24% of AI professionals in India are women

Tenure

• On average, AI professionals in India have joined/transitioned to their current role in the last 3 years
• 67% AI professionals in India have joined/transitioned to their current role in the last 2 years
• The numbers reveal AI is a relatively emerging technology and a large number of professionals are gradually gravitating toward it

Education

• 48% of AI professionals have a Master’s/Post Graduation degree
• 5% of AI professionals in India hold a Ph.D. or a Doctorate degree

AI Companies in India

• The 10 companies that employ the most AI professionals in India are Accenture, TCS, Cognizant, Infosys, Wipro, IBM, Microsoft, Amazon, Capgemini & HCL Technologies
• More than 1,000 companies in India claim to work on AI in some form. This includes a small number of companies into products (chatbots, AI-powered visual search and recommendation engine) and a larger chunk offering either offshore, recruitment or training services
• There is an increase of almost 30% year-over-year in the number of companies setting up dedicated AI teams in India
• Moreover, the number of vertical AI companies in India are still very few in number, compared to the strength of AI companies around the globe. Even in analytics, India accounts for just 8% of global Analytics companies

Company Size

• On average, Indian AI companies have 87 employees on their payroll
• Almost 85% of Analytics companies in India have less than 50 employees

AI Talent Divide Across Companies 

• Almost 37% of AI professionals in India are employed with large-sized companies(with a total employee base of 10k+)
• Mid-size organizations (total employee base in the range of 200-10K) employ 29% of all AI professionals in India.
• Startups (less than 200 employee base) employees form 34% of AI professionals in India.
• A large percentage of AI professionals are absorbed by digitally mature big tech firms that are increasing their investment in AI in India.
• This is also a clear indication of the AI talent divide between enterprises and startups in India, which will only continue to widen further

Cities

• Bengaluru leads the cities in terms of the size of the AI ecosystem. 32% of AI professionals in India are working in Bengaluru
• This is closely followed by Delhi NCR at 22%

AI Salaries in India

• The median AI salary in India is INR 14.3 Lakhs across all experience level and skill sets.
• Around 40% of AI professionals in India have an entry-level salary of 6 Lakh onwards
• Almost 4% of AI professionals in India command a salary higher than 50 Lakhs

Average Salary Trend Across Cities

• Mumbai is the highest paymaster in AI at almost 15.6 Lakhs per annum, followed by Bengaluru at 14.5 Lakhs
• Chennai is the lowest paymaster at 10.4 Lakhs

AI Jobs in India

• While it is difficult to ascertain the exact number of open AI job openings, according to our estimates, close to 4,000 positions related to AI are currently available to be filled in India. Open AI jobs is a different metric than new jobs advertised per month
• Compared to worldwide estimates, India contributes 10% of open job openings currently. Growth in the number of AI jobs globally was much higher than in India
• 10 leading organizations with the most number of AI openings this year are – IBM, Accenture, Amazon, Fractal Analytics, Societe Generale, SAP Labs, 24/ 7 Customer, Atos, Nvidia & Tech Mahindra
• The top skill sets that AI employers are looking for are Machine Learning, Natural Language Processing, Neural Networks, Analytics, Cloud Computing & Pattern Recognition
• Almost 92% of AI jobs advertised in India are for full-time roles; rest are part-time, internships or contract basis jobs

AI Jobs By Cities

• In terms of cities, Bengaluru accounts for around 33% of AI jobs in India. This is down from 37% last year
• Delhi/NCR comes second contributing 30% AI jobs in India. This is up from 23% last year
• Approximately 12% of AI jobs are from Mumbai, almost the same as last year

Experience Requirement By AI Jobs

• Around 43% of AI jobs are looking for candidates with less than 5 years of experience
• 6% AI jobs are for freshers
• 57% AI job openings are for professionals with more than 5 years of job experience

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The post The Hitchhiker’s Guide to Artificial Intelligence 2018-2019: By AIM & Great Learning appeared first on Analytics India Magazine.

With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.

With data championing technological innovation, analytics and AI has proven to be a competitive advantage for businesses across functions, such as sales, marketing, finance, customer service and IT.

Today, Data Science and AI have seeped into many business functions and we are seeing increased collaborations between leading companies and Data Science Service Providers, specialising in providing tailored solutions.  Another key takeaway is how companies are planning to increase their IT budgets on analytics and data science.

A majority of our respondents believe the growing adoption of AI and analytics has become a game changer across business functions and there will be an uptick in niche sectors such as sports. A key trend this year was core business processes being redesigned around AI to deliver advantages beyond cost savings. RPA has been widely deployed to automate well-defined, rule-based tasks and the spurt of virtual assistants in banking has spawned a new age of Conversational AI in finance. On the other end of the spectrum, we will see an increased AI-cloud interdependency which in turn will fuel the growth of public cloud companies. Organisations are also realising the benefits of cognitive technologies to provide better insights. This year we collaborated with AnalytixLabs, a leading Data science training institute to bring out key trends.

Check Out The Top Trends In Analytics And Data Science To Dominate In 2019

Trend #1: More Industries Are Going To Utilise Analytics & AI In A Big Way

Enterprises in 2019 and beyond will continue to focus on advanced solutions and adopt technologies like artificial intelligence and machine learning to deliver better customer experience. The technology landscape will also transition to an API-based economy with AI and analytics becoming more pervasive.

In the coming year too, more and more industries and services including automotive, healthcare, broadcast and telecommunications, education, finance, consumer electronics and multimedia will adopt and implement AI to enhance customer experience, optimise and automate processes.

-Nitin Pai, Senior Vice-President, Head of Marketing and Strategy, Tata Elxsi

AI in the Automobile segment is showcasing Automated Guided Vehicles (AVGs) to move materials around automotive plants. Multi-recognition systems monitor and analyse data to derive driver’s attentiveness or excitement level. AI enabled wearable IOT sensors such as H-Vex to protect the workforce ensuring safety and increased productivity.

-Dr Muthukumaran Balasubramanian, Head – BigData, HTC Global Services

While we have seen industries like financial services, consumer product goods, telecom, healthcare etc. embracing analytics in their decision making, the adaptation of data-driven decisions will see an increased application in sports in different areas from performance analysis to fan engagement.

-Indranath Mukherjee, AVP, Head, Strategic Analytics, AXA XL

In 2019, Credit Risk Analytics will deep dive in the Lending algorithm of New to Credit (NTC) using latest AI/ML tools. While several fintechs have come up with incremental data enrichment for this segment, Banks & NBFCs will soon see a crystallised analytical way of managing NTC customers through advanced analytics.  

-Anand K Sundaram, GEVP- Data Analytics Center, YES BANK

The acceptance of AI, ML & DL started to happen in the last few months and now will become the key to solve business problems making it the way of life at organisations adopting analytics. The acceptance and application will increase significantly in global and Indian context solving business problems, developing new use cases and developing innovative products & solutions based on that.

-Gurpreet Singh, Director & Founder, G-Square Solutions

Trend #2: Deploying Models For Real-time Use-cases

The emergence of real-time analytics has become the key to unlock insights and up to 70 percent of organisations have increased their spending on real-time analytics. The shift towards real-time analytics is spurred by the growth in the number of connected devices, which has driven enterprises towards real-time edge analytics which is helpful in finding correlations and hidden patterns, in turn helping business leaders make better, informed decisions. In a similar vein, the rapid development of connected devices and IoT applications has pushed centralised cloud computing to the edge.

In consumer Internet companies such as from the domain of e-commerce, machine learning models are being deployed into the real-time serving infrastructure, whereby based on the user’s actions within the session, propensity scores are being computed to determine the best set of product recommendations along with dynamic pricing. This is being enabled by building models using batch data, but performing scoring in real-time. The challenges are how to employ models with enough sophistication to make a significant impact, yet have a low latency response to the in-session scoring.

-Nitin Seth, CEO, Incedo Inc.

With the explosion in the number of IoT devices, there is a need to harness the power of sensor data to produce meaningful real-time insights. In 2019, move from batch to stream data processing to get real-time actionable insights is going to be massive. More and more companies will look for real-time data analysis for events that need to be detected right away and responded to quickly.

-Akhilesh Ayer, Head, Research & Analytics, WNS

Real-time analytics will be another disruptor in the coming years, we haven’t seen much of it yet. This means using runtime data from dashboards to make decisions.  One good use case could be traffic monitoring for designing intelligent street parking systems.  

-Mohit Kapoor, CEO, DBS Asia Hub 2 (DAH2)

Trend #3: Data Analysis And Informed Customer Space

2019 will be the year when organisations will put “consumer” at the heart of digital transformation. Going forward, consumers will become a part of the larger strategy around digital transformation with enterprises building new business models to engage users better. The study forecasts a trend where enterprises will put data back into the hands of users – the real “data owners” to drive better user experiences.

The business environment for the insurance industry has been challenging. Data will build the foundation for an effective customer engagement strategy of insurance firms. The two major segments of analytics in the online space – group dynamics and individual behaviour, will continue to impact the process of detailed analysis, product development, personalised insurance products, strategy and creating customer-connect campaigns accordingly.

-Vijay Sinha, MD & CEO, COCO by DHFL General Insurance

As opposed to today’s scenario, customer data ownership will move from businesses and back towards customers. We foresee new services that will emerge and empower customers to monetise their data and rent it to companies, with the capability to withdraw its use when they decide to. Since data is the fuel behind powering AI, customers will begin to realise the power they have to drive their AI-based experiences with data control and data management practices.

-Suman Reddy Eadunuri, MD & Country Head, Pegasystems India

In the past machine learning output, especially deep learning output was more of a black box as far as providing an explanation to the users of the product/system. However, more and more machine learning systems are evolving to also provide a tie into user’s history or behaviour in addition to providing the actual product recommendation. With this tie-in, the users get a context of why something is being recommended to them, and are not surprised by particular user experiences presented to them by the consumer Internet company/interface. This is very important to retain the user’s trust in the company.

-Nitin Seth, CEO, Incedo Inc

Trend #4: More Investment In Enterprise-Grade Data Infrastructure, Platforms & Cloud

With increased investment in cloud and quantum computing, business leaders emphasise an increasing AI interdependency on the cloud. In fact, we believe AI services have fuelled the growth of public cloud providers, especially AWS, Microsoft, Google and Alibaba. Going into 2019, we will see an increased AI-cloud interdependency and as we observed in 2018, leading cloud giants are pursuing an AI-lock in approach by providing open source AI-related services. The next year will see AI platforms dominating the public cloud market and cloud providers, especially Google, AWS and Microsoft will further expand their AI cloud portfolio. A key reason why enterprises are leaning towards public cloud is that it is impossible to build in-house, scalable AI systems and the general sentiment is that AWS leads in the cloud and AI deployments.

2019 will witness increased growth of Big data platforms into medium & small size organisations. Big data prominence over the last few years has led to the generation of an enormous amount of data. This data has a colossal potential for analysing existing and new consumer patterns, research, identifying new trends etc. Converting data potential to reality will need an exponentially improved speed of computing, hence the rise of quantum computing. Companies have started investing in this space, however, results are yet to be seen.

-Sudhanshu Singh, Senior Vice President, Analytics and Research, Genpact

Public clouds will increasingly be the host platform for AI, will evolve to reduce complexity, and will have reduced reliance on IT departments. Powerful GPU instances, flexible storage options, and production-grade container technology are just a few of the reasons that AI applications are increasingly cloud-based. For engineers and scientists, cloud-based development eases collaboration and enables on-demand use of computing resources rather than buying expensive hardware with a limited lifespan. Cloud, hardware and software vendors are recognising, however, that this technology stack is often difficult for engineers and scientists to set up and use in their development workflow.

-Paul Pilotte and Bruce Tannenbaum, heads – Data Analytics and AI marketing, MathWorks

Trend #5: Analytics & AI Is Becoming Increasingly Pervasive & Transparent

2018 was a year when analytics and AI showed serious commercial applications and usability. We have observed a shift how technology companies are using AI and analytics to significantly improve their marketplace opportunities. As we move forward in 2019, we see an emergence of cognitive technologies, which go beyond automation to computer vision, deep learning and language technologies which include speech recognition, sentiment and text analytics, natural language processing and generation. As we press forward, we will see a rich ecosystem of platform companies offering AI applications with high business value.

AI is gaining a nearly ubiquitous presence, and machine learning and deep learning are getting more sophisticated. As we enter 2019, we will see a rise of AI and cognitive platforms aimed to help businesses mine and analyse massive amounts of unstructured data (in form of text, images, and videos) to derive actionable insights, a thing we’ve not been able to unlock in the past.  With this greater depth, we could see more commercial use cases of image and speech analytics in the nearer term.

-Akhilesh Ayer, Head, Research & Analytics, WNS

As AI Augmented analytics (predictive & prescriptive) becomes more mainstream, there’s going to be a much wider audience than the data scientists that it was previously limited to. How do you get every employee, who may have never directly accessed data, or has years of “experience” under their belt, begin to trust a prediction or recommendation that analytics software gives them? AI will need to be transparent. Narrative storytelling & explanations on how the software came to a prediction will replace static charts and numbers. AI will need to be measurable. Automated feedback loops that self-report differences between actuals and predicted metrics and drive retraining of the models will be standard features in any AI augmented analytics product. AI will need to be held accountable. Concepts such as “protected variables” (zip code, gender) to prevent bias entering models will be the top 5 requirements in an RFP. Analytics products without these features will not be able to gain the trust of the end users and thus ROI and adoption will suffer.

-Ketan Karkhanis, GM of Einstein Analytics, Salesforce

Businesses are realising that analytics competency cannot be built within a silo in the organisation. Everyone has to talk the language of data. Everyone has to be data savvy. 2019 will be the year that everyone becomes ‘Data Smart’. Data literacy for everyone in the organisation is going to be the mantra for 2019.

-Gaurav Vohra, Co-founder & CEO, Jigsaw Academy

Trend#6: Complexity Necessitates Greater Collaboration

The transforming nature of technology and the changing dynamics will necessitate increased collaborations between engineering teams and data scientists. As we inch towards 2019, business leaders lay down the friction points in product lifecycle management, the complexity of projects and lack of expertise which will have to be resolved to simplify workflows and improve results. What we observed in 2018 was a high adoption of new tools which helped in setting up templates and simplifying workflows and coding.

The increased use of machine learning and deep learning in complex systems will necessitate many more participants and greater collaboration. Data collection, synthesis and labelling are increasing the scope and complexity of deep learning projects, requiring larger and decentralised teams. Systems and embedded engineers will require flexibility to deploy inference models to data centres, cloud platforms, and embedded architectures such as FPGAs, ASICs, and microcontrollers. These teams will also need expertise in optimisation, power management and component reuse. Engineers at the centre of collaboration, developing deep learning models, will need tools to experiment and manage the ever-growing volumes of training data and lifecycle management of the inference models they handoff to system engineers.

–Paul Pilotte and Bruce Tannenbaum, heads – Data Analytics and AI marketing, MathWorks

As data science teams grow, the need for collaboration and version control will become paramount. General purpose engineering tools like Git, JIRA, Trello etc. would become more and more important for data scientists. Also, new tools specific to data science collaboration would get adopted – some of them are already popular – such as mode studio or dbt.

-Ankur Sharma, VP, Analytics, Instamojo

Trend #7: Personalised Products

AI-driven personalisation has become the answer to marketers’ woes and the growing use of analytics and AI is helping businesses to deliver customised communications and services. There is a growing usage of customer segmentation and data to better understand user behaviour and deliver a curated set of services in real-time to serve consumers. Especially in the financial sector, there is a growing uptick in the adoption of analytics to rethink customer engagement and the segmentation approaches.

Customers prefer personalised insurance covers instead of the one-size-fits-all products. Flexible coverage options, micro insurance and peer-to-peer insurance are becoming viable options. Lifestyle apps are re-imagining the insurer-insured relationships. APIs are enabling the creation of insights-driven offerings as they integrate data from multiple sources. Premiums will become highly personalised, enabled by new sources of tech-enabled data such as the Internet of Things, mobile-enabled InsurTech apps and wearables.

-Anand Pejawar, President-Operations, IT & International Business, SBI Life Insurance

Some lenders are already personalising the targeting for sourcing loan applications. However, we are yet to see personalised interest rates beyond broad segments. We are headed towards a world of ultra-personalisation where a loan’s approval, the amount, and the interest rate would depend on the applicant characteristics, the purpose of the loan, the tenure requested, and the external environmental factors of that time.

• Pre-loaded/Dynamically-loaded EMI cards with personalised interest rates will become more ubiquitous
• Personalised restructuring of loans/add-on loans will become a norm
• P2P lending and newer NBFCs in the form of start-ups and big-tech will increasingly compete with traditional banks and NBFCs, driving a reduction in interest rates and increasing the pace and personalisation

-Dr Satya Gautam Vadlamudi, Head of Data Science and Artificial Intelligence, CreditVidya 

Trend #8: AI Will Make Analytics More Human, Not Less

Amidst the ongoing debate about AI syphoning off jobs, there is an increased emphasis on developing human-level AI. Leading tech giants such as Microsoft and Google are setting up ethical committees to monitor how technology impacts human lives and eliminate bias in data. On the other spectrum, we have seen an think tanks across the globe which are working alongside Governments and enterprises to help companies create new economic opportunities for citizens and also set up new ways to benefit the society as a whole.

There is rightful concern about the rise of AI and its potential to eliminate jobs. But in the near future, AI will likely create more jobs than it eliminates. Gartner predicts that in 2020, AI becomes a positive net job motivator, creating 2.3 million jobs while only eliminating 1.8 million jobs. In 2019 and beyond, AI designed around people will have a higher impact than AI that takes people out of the process. By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI as well as data science and machine learning platforms, and of embedded analytics.

-Arun Balasubramanian, Managing Director of Qlik in India

We’re getting past a point where basic intelligence is sufficient for consumer-facing AI. Customers are realising the need for brands to view them as individuals and not of the many customer data records. In 2019, vendors are expected to focus on increasingly humanising AI along with empathy. This could mean picking up clues on customer motivation, their current mood perceptions, or how they could act in certain situations.

-Suman Reddy Eadunuri, MD & Country Head, Pegasystems India

Trend#9: Centralised Data Will Be Replaced By A Single View Of All Data

As data volumes grow high, organisations are moving towards enterprise data management platforms that help businesses gain insights faster by breaking down the data silos. To resolve integration challenges, we will see enterprises bringing data from various sources into one, central repository. As forecasted, a central data store will be the key to fulfil analytical requirements of organisations.

Two massive trends are changing the landscape. First, different vendors are coming together to standardise data models. Cloud-based data sources, in particular, will have more standard formats. Second, and more important, is the emergence of enterprise data catalogues. These catalogues are accessible in a hub, with one view of the entire federated data estate, and deliver a shop-for-data marketplace experience. The more you share, collaborate and use the hub, the more valuable it becomes to the business. Furthermore, it links your analytics strategy with your enterprise data management strategy, as the data becomes analysis-ready. In 2019, the focus will shift from bringing all data together into one place to getting a single view of all data. By 2020, most D&A use cases will require connecting to distributed data sources, leading enterprises to double their investments in metadata management.

-Arun Balasubramanian, Managing Director of Qlik in India

In 2019, we need to watch out for the convergence of various data sets onto single AI platforms. Up until 2018, corporations have been looking for AI solutions in silos – Chatbots, RPA tools, etc. But we missed out the real fodder for AI, DATA! The solutions for 2019 will unite structured and unstructured data to automate entire processes. With the hiring, for instance, bots will read the JD/KRA and the resumes, rank them in order of relevance, reach out to candidates automatically, engage them with chatbots for a first-level screening along with addressing their queries. If the candidate and the corporate are interested, the interview will be scheduled. Following that, if the candidate gets selected the bots can further engage them as an assistant. If all this data is on the same platform the results would be phenomenal.

-Animesh Samuel, Co-Founder & Chief Evangelist, Light Information Systems

Trend#10: Voice & AI Assistants Are the future

If 2018 belonged to AI, 2019 will be the year when conversational AI and digital voice assistants will dominate sectors such as finance and retail. In India, the year was dominated with voice-based digital banking chatbots that are able to provide accurate banking solutions. The trend has been fuelled by tech giants releasing their APIs for voice-based conversational AI, which helps businesses to build intelligent services. Going forward, we will see more industries diving into voice-enabled AI services.

In 2018, nearly 20% of American adults, or 47.3 million people, had access to a smart speaker; nearly anyone who owns a smartphone can use a voice assistant like Siri, Cortana, Alexa, or Google Assistant.  As a group, we’re used to incorporating these assistants in our daily lives; look for that trend to migrate further into our working lives as 2019 progresses.  How will we use these virtual assistants?  In much the same way as we do outside of work: managing calendars and schedules, setting appointments, finding information, and scheduling reminders.

-Suhale Kapoor, EVP & Co-founder, Absolutdata

Conversational platforms will evolve rapidly in 2019, catering to a larger set of Indian languages and will enable more complex conversations with users by leveraging advanced deep learning techniques for NLP. This will also enable analytics applications to percolate deeper towards the base of the pyramid.

–Ankur Narang, Head, Data Science & AI practice at Yatra Online

The usage of AI has picked up in the last couple of years.  Chatbots and voice-enabled digital assistants are gaining in popularity.  This trend will continue and the focus will be on making the responses more contextual and back and forth conversation enabled.

-Gunjan Gupta, Senior Vice President & Head of Analytics at Bajaj Allianz Life Insurance Company Limited  

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The post 10 Data Science & AI Trends In India To Watch Out For In 2019: By AIM & AnalytixLabs appeared first on Analytics India Magazine.

This Data Science Service Providers benchmarking study by Analytics India Magazine and IMS Proschool aims to separate top performing data science providers from other vendors. Our experience shows that customers — enterprises and mid-sized companies alike are exploring and leveraging new technologies — data science and analytics by moving all the major levers — analytics solutions, building a foundation for data infrastructure to track performance and improve services and experience for their customers.

The growing availability of data coupled by high computing power has created an unprecedented opportunity for enterprises to use analytics to improve decision making and align it with the overall business strategy to drive value. As a result, organisations in India are spending billions of dollars to build their analytics capabilities and are increasingly turning to some type of reporting, analytics and data science technology to address key business challenges.

As enterprises strive to take advantage of analytics and data science to increase profitability, drive customer engagement and grow revenue while identifying and minimizing risk, they are tapping into the power of data by collaborating with service providers. Clients are implementing these initiatives in a strategic way, however it still remains to be seen if these efforts to raise analytics quotient will eventually translate into value-add for the end customers.

In turn, CXOs are demanding Data Science Service Providers that can respond quickly and effectively to changing business demands and gradually move into a value creation role. Clients expect stronger industry knowledge from vendors to provide better business context and effectively respond to market opportunities and challenges with agile and collaborative decision-making processes.

AIM’s Penetration & Maturity Quadrant (PeMa matrix) aims to benchmark Data Science Service Providers in India and highlight vendors with more mature capabilities that have the advantage leading the charge around data science services and standardization. The vendor’s thought  leadership and competency is measured over two key parameters— Pe (x) and Ma (y). A balance between both these parameters is essential to respond to client’s business challenges effectively and seize revenue opportunities with analytics. These vendors are also ahead of the curve in terms of industries and geographies served. Analytics and data science solutions from these vendors deliver a comprehensive, unified business analytics system that meet the growing demands of clients and at the same time, provides secure enterprise governance and control.

Market Definition

The Data Science Service Providers study acts a Vendor Benchmark report and analyzes leading players in India. The Vendor Benchmark report assesses the Penetration & Maturity Index of Service Providers and gives strategic insights into their investments, go-to-market approaches and partnering techniques for forward looking organisations and mid-sized firms.

Data Science is the fastest growing technology services in India and globally with the big data analytics market expected to reach $16 billion by 2025. Today, Data Science encompasses services such as big data analytics, business intelligence, data warehousing, machine learning technology, enterprise modernization, and artificial intelligence capabilities to Government and commercial clients. AIM observed that industry investment in analytics and data science is noticeably increasing.

Missing from the PeMa Quadrant are many vendors that we have been watching closely but at this stage, they do not meet all the minimum criteria to participate in the study. AIM sees the emergence of a new area of data-driven vendors that are offering integrated business analytics system with flexible deployment options.

Vendors that do not have a full data science capability, with only limited case management are not included in this study. In the future, we will explore this rapidly evolving market space. Factors affecting the evaluation include the extent of a vendor’s presence in the market, the number of geographies served and the observed momentum in their growth.

PeMa Quadrant

We have deployed an approach —  Penetration Maturity Quadrant (PeMa) to appraise the efforts of data science providers. PeMa maps the effectiveness of service providers on two broad parameters, namely Penetration of (x) and Maturity (y).

To qualify as a service provider that is truly ahead of its competitors in terms of penetration, the companies should demonstrate several enablers that include:  

• Number Of Analytics Employees
• Number Of Clients For Analytics Services
• Number Of Large Clients (Client Listed In Major Bourses)
• Sectors Services For Analytics Services
• Geographies Served For Analytics Services

The Maturity index will measure factors such as:

• Employee Seniority
• Advanced analytics work done
• Dependence on key clients
• Data science product offerings
• Tenure of employees
• Years in Market

This report seeks to lay a foundation to define the PeMa Quadrant as well as emphasise the major enablers that businesses should take into consideration before onboarding a vendor and understand partnering techniques. Through this unique approach, we aim to help business leaders understand and evaluate the essential focus areas of Data Science Service Providers in India across two main categories — Penetration and Maturity. The PeMa Quadrant can also be viewed as an opportunity to plot a strategic path forward. Forward-thinking companies can use the framework to identify the gaps and potential opportunities and gain a comprehensive view of a Service Provider’s analytic/technology capabilities and how it has evolved.

Methodology

The analysis includes operational metrics, maturity in terms of data science product offerings, depth across geographies, acquisition strategies, and the vendors’ initiatives in keeping with emerging analytics and AI trends.

For the Penetration & Maturity Quadrant, AIM built a survey to gather data to analyse vendors. We also received entries from the vendor customer community. Our research team carefully scrutinized and verified all respondent entries to ensure qualified participants are included in the report.

Study Demographics

Our Top Data Science Providers in India 2018 study Big Data Analytics Market Study is based on data and spans organisations size, verticals and functions. Unlike other industry research, this study is an indicator of analytics market dynamics.

Companies mentioned in this report/study are as follows:

The PeMa quadrants have been created using an evaluation matrix and contain four segments where the Service Providers are positioned accordingly.

Pioneers

The “Pioneers” among the vendors in the top right quadrant are those who have a strong market presence across industry verticals, are estimated to have double-digit growth and offer end-to-end analytics solutions, including strategy, solution design, implementation and execution. In terms of Maturity index, the vendors in this quadrant display deep domain expertise, industry-leading analytics solutions and expertise in different industry verticals which makes them the frontrunners in the market. The service providers can be regarded as opinion leaders, digital enablers who lend strategic impulses to the market. The Penetration dimension measures the geographical footprint and diverse set of clients served across the markets. The vendors’ strong geographical presence across Asia Pacific, US and Europe, backed by cross-industry reach gives them the ability to understand business needs across all layers of client organisations.

Genpact: The last few years have witnessed Data Science making quantum leaps into a multitude of domains spanning Banking & Finance, High-tech and Manufacturing, Customer Analytics, Insurance, Healthcare and so forth. Genpact’s vision is to operationalize Data Science for clients from every domain to make faster and efficient decisions. Data Science at Genpact is part of the large umbrella of capabilities and offerings within the Analytics space. They leverage state-of-the-art academic and industrial research to lead clients into a new frontier with solutions developed by tightly infusing domain and software engineering with Data Science solutions at core. Data Science is an interdisciplinary field that incorporates Computer Science, Mathematics and Business Domain Knowledge to derive value from the data. They are proud to say that their Data Scientists are not type casted to solve a particular type of challenge and they are equipped to leverage Deep Learning, Machine Learning, Natural Language Understanding/Generation, Statistics, Optimization, Forecasting and Graph Theory as required. The flexibility to handle structured and unstructured data (text, speech, images and video) while following agile methodology of development helps to glean meaningful impactful insights for clients. Genpact provides these services through their AI platform Genpact Cora – a modular, interconnected set of curated technologies that solve for the data ingestion and availability of scalable compute power (GPU and Big Data) with ease of deploying consumable API by clients in their existing processes/pipelines.

Bridgei2i: BRIDGEi2i is a trusted partner to enterprises for AI-driven Digital transformation. BRIDGEi2i leverages Data Engineering prowess, Advanced Analytics capabilities, proprietary AI accelerators and Consulting expertise to contextualize data, automate actionable insight generation and enable data-driven decisions across the enterprise to accelerate digital transformation. Backed by the global equity firm Edelweiss, BRIDGEi2i is a powerhouse of talented data scientists and AI experts who solve some of the most complex business problems. With a steady focus on AI-powered asset-based consulting, BRIDGEi2i offers tried and tested transformation frameworks and customer-centric engagement models to deliver sustainable business impact. BRIDGEi2i’s proprietary AI accelerators Watchtower, Recommender, Optimizer, and Converser enable the democratization of analytics insights to drive faster and more accurate business actions for digital transformation outcomes.

Consistently ranked by Analyst and Advisory firms among most prominent analytics and AI solutions companies, BRIDGEi2i has been consistently focused on building best in class Data Science and AI capabilities through SCaLA, a progressive capability development program and AI labs, an AI-focused innovation group.

BRIDGEi2i has steadily grown into a 400+ member organization today and is a transformation partner to more than 15 Fortune 500 companies globally. With some of the best minds in the industry and cutting-edge innovation, BRIDGEi2i is poised to cement its position as a leader in the analytics & AI solutions market.

Seasoned Vendors

In the top left quadrant are the “Seasoned” vendors who with their strong analytics and data science consulting expertise and significant portfolio share have consolidated their market presence. The vendors in this quadrant show more depth in the Maturity dimension, with deep analytics and data science practice and domain-based consulting, translating customer demands into solutions. These established vendors new technology investments and solution development capabilities provide a foundation on which to build a better analytics market presence. Moving in the other dimension, Penetration, the seasoned vendors need to grow their market strength, capture share in upcoming growth areas of the analytics market. In fact, they are yet to catch up with the Pioneers in their hold on the global market.

Cartesian Consulting: Mumbai-headquartered Cartesian Consulting specializes in analytics that helps businesses improve customer value, marketing spends, and business decisions. Set up in 2009 they are over 200 people strong, present in 5 offices across India, APAC and North America.

Their Services division provides bespoke analytics to over 50 domestic and global brands, spanning Customer Analytics, MMM and MTA, Promo Optimization, Business Analytics, Pricing and Promo Optimization, Store analytics, Retail analytics and Digital Analytics.

Their new Solutions division blends in AI and ML models into ready-to-use products the first two of which are Segment of One Engine and TheKyte.ai –  Subject Line wizard. Having set up AI lab in mid 2017 they are constantly pushing themselves to convert their IP into consumption-ready solutions.

Their main differentiator is their focus on creating business impact through analytics for clients. They measure contribution to toplines and participate in the outcomes.

AnalyticEdge: They use technology extensively to develop marketing analytics solutions. The core differentiator is end-to-end marketing effectiveness measurement platform called Demand Drivers. It is a DIY (Do-It-Yourself) platform and marketers with only a basic knowledge of data science can build statistical models for measuring the effectiveness of their marketing spend across all marketing channels. Traditionally, marketing effectiveness solutions have been very resource intensive, expensive and slow. However, the automated data ingestion capability in their platform coupled with cutting-edge machine learning algorithms and the user-friendly Simulation and Forecasting capability enables them to deliver cost-efficient, scalable and real-time solutions.

3LOQ: 3LOQ is an AI start-up founded by Carnegie Mellon alum Anirudh Shah and Sunil Motaparti. Having worked in the field of machine learning and data analytics sector for more than a decade, 3LOQ’s leadership has deep expertise in building extremely efficient algorithms that solve multi-dimensional problems. Their flagship product Habitual.AI is the world’s first AI engine that automates the process of building habits. Habitual.AI triggers intent-driven usage at scale by using cognitive computing, analytics and machine learning.

The patent-pending technology currently enables banks to create habitual users across digital and mobile platforms. It learns how customers use a digital banking product and calculate feature recommendations that are most likely to foster habitual usage. Trusted by leading institutions in global banking, Habitual.AI currently services a total base of more than 10M end-users. It consistently delivers business results in the form of more transactions, more monthly active users (MAUs) and reduced customer churn — all by building customer habits.

Growth Vendors

In the bottom right of the quadrant are “Growth” vendors who are potential contenders that will evolve into the next quadrant in due time. Their combination of powerful in-house capabilities and a mature mix of analytics solutions is recognized by customers across domains and has pivoted the service providers in the Growth quadrant. In terms of Maturity dimension, the Growth vendors provide comprehensive analytics solutions and technological frameworks; their consulting teams effectively build solutions which add value to its consulting offerings. The Growth Vendors’ team effectively works across industry verticals to achieve better business outcomes for clients’ engagements. The two key highlights of Growth vendors are the cost-competitive analytics solutions and the domain-led consulting approach. In the Penetration dimension, the vendors lag behind Pioneers and Seasoned service providers with limited geographic presence. For Growth vendors to pivot to Pioneer quadrant, the service providers will have to deepen their industry presence and geographical reach and build a successful client base.

LTI: LTI’s Mosaic is a unique offering that leverages the power of Data, AI and Automation to overcome the challenges of data-driven decision management. The platform is equipped with state-of-the-art data engineering and cutting-edge advanced analytics capabilities. It simplifies “Data-to-Decision” in hyper-distributed data and hybrid-computing environments with the No-code/Low-code concept, powered by AI logistics, automated intelligence & actionable insights. It harnesses valuable data to generate actionable insights that facilitate business transformation and enhance the quality and speed of decision-making. Mosaic suite is cohesive yet modularized, with 5 core sub-products coupled together:

1. Mosaic Decisions: Self-service data platform for your hyper-distributed data needs
2. Mosaic AI: A Catalyst for Enterprise AI adoption
3. Mosaic Catalog: Cognitive data management platform
4. Mosaic Lens: A self-driven business analysis solution to discover data as never before using applied AI
5. Mosaic Automation: AI led Process Automation Platform

Absolutdata: Absolutdata products and services deliver scalable business impact across the enterprise by combining cutting-edge AI and ML with its heritage in analytical frameworks, business understanding and technology. A full suite of AI-powered products and data science services are changing the way global brands make decisions. Absolutdata’s NAVIK AI Platform has pre-built solutions, customizable solutions, enabling services to get an enterprise AI-ready. The growing set of AI-powered SaaS solutions include NAVIK SalesAI, NAVIK MarketingAI and NAVIK ConceptAI. The services teams build custom solutions based on NAVIK AI. Founded in 2001, Absolutdata is based in San Francisco and employs 400 professionals across offices in San Francisco, New York, Chicago, London, Singapore, Dubai and Gurgaon.

The idea of AI coaching has become more central to Absolutdata’s approach as the company has evolved over the past few years.

Absolutdata has created products that bring AI and ML to sales and marketing analytics and forecasting, along with making tactical recommendations.

Tiger Analytics: Tiger Analytics is an AI and advanced analytics consulting firm enabling enterprises to generate business value from data. Today, several Fortune 500 companies engage Tiger to help build cutting-edge solutions and bring in a machine learning and AI perspective to address their business challenges.

Tiger Analytics sets itself apart through a combination of factors with the goal of delivering measurable business impact. It provides a unique approach to blending data science and consulting for business problem-solving. It boasts differentiated talent with 75% of consultants has advanced degrees with learning-focused work culture.

They have in-house assets that reduce project execution times by up to 30% and offer pre-built solutions for key industries such as Insurance, Retail, CPG.

Math Company: In the past, enterprises partnered with conventional service providers to solve problems and incurred ‘economic rent’ but their capabilities remained stagnant. The Math Company’s mission is to enable viable and valuable data and analytics transformations customized to an enterprises’ needs. This transformation journey includes a customizable mix of design, solve and enable services.

Design includes analytics maturity assessment, roadmap design where they infuse an ‘expert practitioner’ mindset in developing key strategies. Solve includes architecting the data governance and structure, solutioning for complex business problems using advanced approaches. Enable services are one of their key differentiators helping Enterprises truly advance their analytics capabilities and maturity. This includes enterprise-level global analytics awareness programs, experiential learning modules, design and deployment of hiring processes.

In just two years, they have managed to work with over 25+ large Global Enterprises across 10+ countries and grow our strength to 200+ employees. Some examples of their work include building Analytics CoEs across the globe, transforming decision making by developing solutions using new age data sources (ex: IoT systems) & advanced analytics approaches (ex: Deep learning, System Dynamics).

Course5 Intelligence: Course5 Intelligence drives digital transformation for businesses through advanced Digital Analytics and Artificial Intelligence (AI). They use 360-degree data synthesis and AI-driven digital engineering to help clients make the most effective moves related to their customers, markets and competition.

They provide holistic business solutions that help clients leverage and optimize their existing data and other investments to improve their top line and bottom line. They have a strong focus on customer-centricity that has resulted in several multi-year engagements with many clients.

They create value for our clients through Deep industry and domain expertise (Digital + Tech + Business + Data), Digital Suite and Research AI Suite to accelerate solutions, Solution toolkits and frameworks for specific business questions, Critical understanding of advanced digital technologies related to Analytics and Data Science, Marketing, and Business/Competitive Intelligence, Application of state-of-the-art AI and next-generation technologies for cognitive automation and enhanced knowledge discovery and more.

Challengers

 “Challengers”, in the bottom left of the quadrant are a new breed of competitive Service Providers who with their robust training and delivery approach, go-to-market abilities have emerged as contenders in the analytics and data science space. These vendors are often niche players, serving limited geographies but they have the potential to catch up to Growth vendors by improving their market presence and optimizing their portfolio. In both the dimensions, Penetration and Maturity, the Challengers lag behind other vendors in the quadrant due to their company size and breadth of offerings. By setting up exclusive pre-sales and consulting teams, Challengers can expand their geographic footprint and step ahead in the game.

Lymbyc: Lymbyc took shape with the idea of infusing cutting edge technology into insight generation. Their vision is to empower the business leader with insights at the point of decision making, and in real time, as opposed to the existing processes which run into weeks and months.

Their unique proposition lies in how they have utilized artificial intelligence, big data and machine learning in creating what they call the “World’s first virtual analyst” – Leni. Leni is a world-class analytics product that can crunch massive data into insights by leveraging Artificial Intelligence framework on a Big Data layer. Leni curates embedded intelligence across all data sources and provides predictive insights driven by its adaptive ML engine. With an aim to democratize analytics in the most consumable form, Leni, powered by its proprietary NLP engine, allows business users to ask questions in plain English and receive actionable insight across analytics complexity –  exploratory/descriptive, diagnostic, and predictive within seconds.

Datalicious: Sydney-headquartered Datalicious which operates out of the APAC region uses scientific methods to generate valuable insights to optimize marketing campaigns and improve the customer’s brand experience. Their attribution model takes into account user behaviour which is state of the art. Their core product offering OptimaHub is built for Media Attribution and Media Mix Modelling allow accurate insights for decision-making.

FN MathLogic: FN MathLogic has deep domain expertise in the area of BFSI, healthcare and telecom. They are a niche data science organization specializing in providing cutting edge solutions to complex real world business problems. Their key differentiator is their expertise in the use of advanced analytics including Deep Learning & Machine Learning. The company aims to provide customised solutions to clients and they believe each client and market requires a specific approach to understand the nuances of their issues. Their analytical solutions are tailored to the client’s business objectives.

G Square: They are an AI-driven analytics company catering to the financial services Industry in India and offshore markets, providing descriptive, prescriptive and predictive analytics solutions in the B2B space for clients in Banking, Fintech, AMC, Wealth management & NBFC space. They are a product driven company supported by services model. Their key products are Narrator, Bigdator and Derivator. The product model is SaaS based pricing. Some of their key clients include Axis Bank, YES Bank, RBL Bank, APICORP Saudi Arabia, SBI MF, Invesco MF, Rubique, Capital First etc.

With strong expertise in Artificial Intelligence, Machine learning and apply strong data engineering, their uniqueness lies in using their own in-house technology and proprietary algorithms for building models along with conventional methods. They have an output driven work approach which provides a lot of comfort to the clients. Repeat business from clients is a testimonials to the same. Their proprietary products, plug and play analytics approach & relevance to client needs are key differentiators that makes them stand out with respect to others in the Industry.

TechVantage: TechVantage is a Product engineering company specializing in building software powered by Analytics, Machine learning and Artificial intelligence. They build products for clients and also build products that they take to market.

Their key differentiation lies in the fact that they are a full stack data scientists. They just don’t build data models but have skills to help operationalize the model and make it usable in a business context. With a good exposure and deep expertise, they package model into a usable piece of software that can be applied in day to day operations.

BluePi Consulting: Provides advanced analytics solutions for different industry domains, in the area of Sales Prediction, Process Automation through Machine Learning, NLP, Customer Segmentation and Risk Profiling. Established in 2012, BluePi Consulting, based out of India has its own analytics solution piStats, which provides analytics, push notifications and personalized recommendation requirements.

SIBIA Analytics: They offer analytics products and platforms in a SaaS model so that businesses of any size can benefit. They are a complete solution agency that works across data aggregation, data management, analytics and insights, and data visualization through proprietary web hosted dashboards. They have built our solution using open source, cloud and big data technologies.

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Analytics India Magazine Startup Funding Report 2018 highlights the latest trends in funding in 2018.  AIM conducted an independent study and tracked the funding of AI & Analytics startups over the last one year and looked at which domains attracted big ticket investments, deals by geography and which startups won the biggest war chest. This list does not include AR/VR or hardware focused startups in India. The data is only reflective of reports published in the last one year and doesn’t include any non-public information.

The startup landscape in India is being transformed by accelerating investment and deal activity around intelligent automation and artificial intelligence, machine learning and big data. In 2018, startups raised an all-time high capital, registering a 368% growth from 2017. In 2018, startups with operations in India and globally raised approximately USD$ 529.52 million in funding rounds and this data includes startups with investment at varying stages of development, from pre-seed to well-funded companies. California and India based Automation Anywhere bagged the biggest cheque of $300 million from SoftBank Vision Fund. As compared to 2017, where the startups received an aggregate investment of US$113 million, funding rose to a record 368% in 2018.  

The data clearly indicates that startups that had AI as a core product or are developing narrow AI tech bagged the heaviest funding from leading VC firms and investors who are investing heavily in deep tech startups in automation, enterprise AI and big data. It also underscores much of the financing is happening in domain-specific breakthrough technologies, and not general-purpose AI tech.  

2018 also saw one of the biggest mega-rounds led by top-tier investor SoftBank Vision Fund for San Jose headquartered Automation Anywhere which secured a sizable $300 million investment from the Vision Fund in November 2018.

Key Highlights

• 2018 saw the biggest funding rounds of all, grossing USD$ 529.52 million, 20% less than the combined share of deal volume over the last four years — 2014-2017, where the total investment was USD $661 million.
• Compared to the drop in 2017, where funding was almost halved vis-a-vis 2016, 2018 saw a 368% increase with investment of USD$ 529.52 million in applied technology sector with AI, ML and Data Science being the major domain and sub-domains. This is a 4x increase as compared to last year’s numbers
• All the high-growth companies that attracted big ticket funding had AI as a core product or are applying AI, ML technologies to verticals like healthcare, finance, supply chain and energy
• Domain centricity was the key trend in 2018 with domain-specific applications such as data centre automation, energy efficiency and supply chain management receiving the highest capital infusion. This shows these are the domains that show maximum promise for VCs who see the potential in these commercial applications and believe it will turn into a viable business model
• Bangalore, the startup capital of India eclipsed other Indian cities by a staggering margin, grossing $133 in aggregate funding. India’s Silicon Valley was followed by Chennai at $35 million and Gurugram at $29.5 million. It is yet to be seen whether Bangalore, the hub of groundbreaking startups will be able to translate the capital into growth and customer acquisition
• Outside the traditional hubs, Chandigarh-based and IIT Kharagpur-incubated AgNext Technologies, agritech startup received an undisclosed amount of funding from Omnivore Partners
• Biggest dealmakers of 2018 are SoftBank Vision Fund (the biggest purse of all – $100 billion Vision Fund), Dell Technologies Capital, TPG Growth, Bain Capital Venture, Hyde Park Venture Partners and CLP Holdings Group, Innogy, Orsted, Tenaska followed by Jungle Ventures and Lakestar
• Venture investment at the early stage (Series A, Series B and seed funding) showed a sharp rise as compared to late stage financings (Series C and Series D)

The Bull Run Continues for Bangalore Startups

Big ticket funding was led by US-Bangalore startups that included RPA player Automation Anywhere ($raised 300 million from Soft Bank Vision Fund), enterprise AI specialist Noodle.ai (raised $35 million from Dell Technologies Capital and TPG Growth), AI-based grid analytics provider AutoGrid which works on energy solutions (raised $32 million Series D from CLP, Innogy, Orsted and Tenaska. Meanwhile, energy analytics firm Bidgely secured $27 million in Series C (raised funding from $27 million from Georgian Partners). Healthtech startup SigTuple that has developed an AI-powered platform Manthana SigTuple (raised $19 million in series B funding round from Accel and IDG Ventures). Note: All the top 4 most-funded startups, excluding SigTuple are headquartered in California with engineering units in Bangalore.

2018 – AI showed promise with November recording highest deal volume

The year started on a mild note with $28 million deal volume in January. SoftBank Vision Fund’s deal in November was the highest ever funding seen in the year, taking the deal volume to $337 million in November. Earlier in 2018, June registered a high deal volume of $60.95 million followed by September, $46.55.

Technology Growth Across Sectors – Automation, AI, ML big winners

The biggest deals of 2018 show there is a lot of optimism around AI, automation, machine learning, big data and analytics. The trend is clearly towards AI-driven investing and this can also be seen from a domain point of view where narrow domains, such as energy and automation specific applications being the most funded. The new capital is not only driving the valuation of these companies but also proves that all the rumblings about AI are true.

Investment by Cities – Bangalore continues to be the hub

Bangalore has the biggest war chest with an aggregate of $113 million capital. This number doesn’t include the biggest funding round of all – San Jose headquartered Automation Anywhere’s $300 million funding. There is a wide gap between Bangalore and Chennai which received $35 million, followed by Gurgaon that grossed $29.5 million and Mumbai at $6 million while Delhi recorded a deal volume of $1.7 million. However, outside the major hubs, other cities that saw VC momentum was Coimbatore’s social media analytics platform Synctag which raised $307,600 and Chandigarh-based AgNext which received an undisclosed amount of funding.

Early Stage vs Late Stage funding

Even though the biggest deals were closed in late stage, early stage and seed stage funding surpassed late stage thanks to more deals, not higher amount. The boost in early stage is from higher number of deals, with Series A leading by 36.4% followed by 27.3% in seed stage investments. Series B saw 18.2% investment while late stage (Series C, D) recorded 4.5% each respectively.

Top Dealmakers 2018 – SoftBank’s Masa Leads The Way

SoftBank with its ambitious $100 billion Vision Fund led by Masayoshi Son and Rajeev Misra has disrupted the venture capital market with its staggering investments, allowing companies to stay private longer. In India, SoftBank has an impressive investment portfolio, backing the biggest consumer internet companies and this time too, it gave a major fillip to RPA giant Automation Anywhere by writing a $300 million cheque. Given Masa’s funding pattern, he always puts a safe distance between his competitors.

The second major investment, $35 million was by Dell Technologies venture investment arm and TPG Growth in Noodle.ai. Bain Capital Venture and early stage VC firm Hyde Park Venture Partners also invested $35 million in Four Kites. This was followed by consortium of Denmark’s Orsted, Hong Kong-based CLP, US energy company Tenaska and German energy company Innogy which made an investment of $32 million in AutoGrid. Meanwhile, Jungle Ventures and European Venture Capital firm Lakestar invested $29.5 million in AI low-code platform Engineer.ai.

Year-on-year comparison – 2018 grossed an all-time high funding

The last five years has seen the industry build on its analytics innovations and lay the foundation for AI. There has been a steady rise in investment from 2014 which clocked $75 million in investment, while 2015 saw a 70% growth in funding. 2016 saw $213 million being poured into startups while 2017 saw funding almost halved by $100 million. A key reason is that in 2016 companies were tinkering at the edges of AI, and 2017 it moved from PoC stage to production and now we are seeing full-fledged commercial applications. Also, one of the key takeaways of 2018 is that high-growth startups and companies that are fostering AI innovation are also proving it with their robust financial performance, a major reason why investors and VC firms are betting big on the high-growth companies.

Bottom funded startups of 2018

At the bottom of the stack are Bangalore-headquartered HR tech startup Skillate that raised $200k, AI-driven conversational platform Trilyo targeted at the hospitality sector, which raised $250k in 2018. These startups are followed by Tamil Nadu based social media analytics startup Synctag ($308k) while restaurant analytics company Spoonshot (DishQ) raised $400k in pre-seed funding followed by Bangalore-based Mate Labs that raised $550k in seed funding.

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Last year, researchers at Google applied the “cocktail party effect” observed by human beings to machines. For instance, in a party, there are so many voices, but we only listen to the person we like by focusing our mind on that face.

We all know the idea of speech separation is not new. Few years ago, work on speech-separation and audio-visual signal processing was already accomplished by researchers using neural network model. But there were some limitations in these models such as they are speaker-dependent where a dedicated model must be trained for each speaker separately which limit their applicability. To overcome this speaker-dependency, last year, the researchers at Google came up with this new deep-net based model to address the problem of speaker-independent AV speech separation.

The Audio-Visual Speech Separation Model

The interesting fact about the research is that the focus of the researchers was not only on the speech but also on the visual cues, for instance, the subject’s lip movements including other facial movements that results to what he/she is saying. The visual cues are used to focus the audio on a particular subject who is speaking, thus improving the quality of speech separation.

A collection of 100,000 high-quality videos of lectures, how-to videos and TED-talks have been collected from YouTube to generate training examples. These videos help in extracting clean speech without any extra sound like music, audience running in the background. This clean data known as AVSpeech is then used to generate a training set of “synthetic cocktail parties” by mixing face videos and their corresponding speech from separate video sources along with non-speech background noise from Google’s AudioSet.

Researchers developed a multi-stream convolution neural network-based model by using this data in order to split the synthetic cocktail mixture into separate audio streams for each individual speaker in the video.

The Model Architecture

The input for this network were visual features extracted from the face thumbnails of detected speakers in each frame. For training the model, the network learns to separate the visual as well as the auditory signals. After separating, it fuses them together to build a joint audio-visual representation which helps the network to learn to output a time-frequency mask for each speaker. These output masks are multiplied by the noisy input spectrogram which describes the time-frequency relationships of clean speech to background inference.

Use Cases

There are multiple instances where the CNN model has been implied such as given below

• Video conferencing (Click here to watch the video)
• Sports debate (Click here to watch the video)
• Noisy Cafeteria (Click here to watch the video)

Advantages

This method for isolating single speech in a noisy environment helps in various ways such as

• Speech enhancement and recognition in videos
• Enhancement in video conferencing
• Improving hearing aids
• Speech separation in the wild where there are multiple people speaking, or undisputed video, etc.

The post How Google’s NN Model Can Capture Audio Source By Simply Looking At Human Face appeared first on Analytics India Magazine.

A primary survey was conducted in August through September 2018, where 961 current and past students from 18 cities in India gave opinions on data science courses they attended. Out of these entries, few dozens in-depth interviews were done to understand the reasons for the submissions. This exercise helped in validating the data and providing a rationale for the rankings, wherever required.

A dedicated online questionnaire was created and the link was sent to more than 30 data science schools, of which 21 responded within the stipulated time.The participants were asked to fill an elaborated form with four key parameters — course content, faculty, student experience and other attributes such as external collaboration. Some of the other details under these parameters were course comprehensiveness, post-completion engagement of students, capstone projects and others.

Seven schools were rejected because of incomplete data, lack of supporting documents or not fulfilling the eligibility criteria. The eligibility criteria for this ranking was 1) The course should be a long term program in data science/ analytics (atleast 5 months), 2) Should be executed by (or in collaboration) with a university.

Information collected from the schools was combined with information received by the students.  Out of those, we bring to you top 10 courses which have been analysed thoroughly by our team of experts. Students feedback and expert advice were also taken into consideration for the overall ranking process. Each of these parameters have been ranked on the scale of 1-5 where 1 is for worst and 5 for best. The institutes that could not be a part of the list either did not participate in the ranking process or could not make it to our list.

For top 1o courses on Artificial Intelligence in India, check this.

See our Top 10 Full Time Data Science Courses In India- Ranking 2018 here.

For top 10 data science training institute in India 2018, check here.

1. Post Graduate Program In Business Analytics And Business Intelligence (PGP-BABI) By Great Learning In Association With Great Lakes Institute of Management

Founded: 2013

Mode of delivery: Blended

Course duration: 12 Months

Number of hours: ~440 with 240 hours of classroom learning and 200 hours of online learning

Cities of operation: Mumbai, Bengaluru, Delhi NCR, Chennai, Hyderabad, Kolkata, Pune, Online

Course Fees: ₹ 395000 + GST, ₹ 170000 + GST (Online)

Great Learning is an ed-tech company that offers programs in career critical competencies such as Analytics, Data Science, Big Data, Machine Learning, Artificial Intelligence, Cloud Computing and more. Great Learning programs are taken by several thousands of professionals every year by a network of 400+ Great Learning Gurus. They boast an alumni network of more than 3000 professionals working across organisations in India and beyond.

Parameter 1: Course Content (Rating 4.9)

Comprehensiveness: PGP-BABI uses a combination of learning methods such as classroom teaching, self-learning through videos and reading materials, team-based problem solving and others. The exhaustive course curriculum includes foundation (foundation in Statistics using R, business and management concepts), analytical techniques (R, Python, Tableau SAS) and domain application and industry exposure. They have a capstone project of 3 months, while the course is updated every 6 months.

Accessing students at the end of the course: Following a continuous evaluation, the students participate in group projects, individual assignments, quizzes and group discussions in every course. Students are tested for their conceptual understanding and ability to apply various analytics techniques in real-world problem.

Learning resources: Books, downloadable resources, LMS, videos, question papers. Students can also work on a number of practice exercises and datasets provided by them.

Parameter 2: Faculty (Rating 4.9)

Total no. of faculty members: 87

Total no. of faculty members with a PhD: 31

Total no. of faculty members with industry experience: 87

Student to faculty ratio: 16:1

Parameter 3: Student Experience (Rating 4.8)

Percentage of students who completed the course: 91%

Post-completion engagement: Every graduating student becomes a Great Lakes Alumni and are invited to all alumni and networking events. They also have lifetime access to a repository of learning material which is continuously updated.

Placement assistance: They provide full placement assistance. 66% of the alumni are working across companies such as Deloitte, KPMG, Gartner, RBS, Cognizant and others.

Parameter 4: Other Attributes (Rating 4.9 )

Entry criteria for students: Industry experience required + Personal Interview

External collaboration: They have collaborated with over 400+ industry experts from companies like Microsoft, Accenture, Deloitte, EXL, WNS, Cognizant, American Express, Absolute Data, HSBC, etc. who design, deliver, and endorse the program. Other notable collaborations are with Bajaj Allianz, WNS Global, XL Catlin for Ideathons, Hackathons, live capstone projects and customized training programs

The overall rating is 4.87

2. Post Graduate Program in Data Science and Machine Learning (PGPDM) By Jigsaw Academy And University Of Chicago

Founded: 2011

Mode of delivery: Blended

Duration of the course: 10 months

Number of hours: 650

Cities of Operation: Bangalore, Delhi NCR, Hyderabad

Course Fees: ₹ 3.65 lakhs + taxes

Jigsaw Academy has been key in contributing to the evolution of analytics talent in the country, having produced over 50,000+ data scientists across the globe. They offer training on multiple popular & niche tools in Data Science, Machine Learning, Big Data and AI technologies that are used globally.

Parameter 1: Course Content (Rating 4.9)

Comprehensiveness: PGPDM includes exhaustive coverage of Data Science and Machine Learning (R, Python & SAS), Big Data (Hadoop, HDFS, Pig, Hive and Spark), and Visualization (Tableau). The program starts from the basics of statistics and covers the entire gamut of descriptive analytics, predictive analytics as well as AL & ML. It includes hands-on program from case studies across BFSI, retail, telecom, supply chain, HR and other industries. The capstone project is for more than 3 months and course is update every 6 months.

Accessing students at the end of the course: PGPDM has a multiple-assessment policy, which includes case studies, assignments, capstone projects, and more. Only those students who pass graded assignments in all the modules and those students who complete corporate projects as per the company’s satisfaction are given the final certificate.

Learning resources: Downloadable resources, LMS, videos, industry lectures, capstone projects, instructor-led classes, assignments

Parameter 2: Faculty (Rating 4.8)

Total no. of faculty members: 60

Total no. of faculty members with a PhD: 20

Total no. of faculty members with industry experience: 60

Student to faculty ratio: 1:5

Parameter 3: Student Experience (Rating 4.7)

Percentage of students who completed the course: 95%

Post-completion engagement: They are a part of the alumni network, invited as guest lectures for future batches & become a part of Jigsaw mentorship program

Placement assistance: Provided full assistance through alumni network, resume designing and helping them with interview process

Parameter 4: Other Attributes (Rating 4.7)

Entry criteria for students: Application + Interview

External collaboration: They have tie-ups the University of Chicago Graham School, along with industry associations with companies like Axteria, Analytics Edge, Tata Motors, Smart Cube, Data Semantics for projects and hiring.

The overall rating is 4.77

3. Post Graduate Certificate Program In Data Science & Machine Learning By Manipal ProLearn In Association With Manipal Academy Of Higher Education

Founded: 2016

Mode of delivery: Online

Duration of the course: 6 months

Number of hours: 650

Cities of Operation: Bangalore, the program is online and hence applicable irrespective of location

Course Fees: ₹ 84,000 + taxes

A part of MaGE, Manipal ProLearn is a pioneer in higher education and allied services. It offers a variety of cutting-edge learning solutions and professional certification courses across IT, Digital Marketing, Data Sciences, Project Management, and others. The Academy of Data Science runs the popular data science certification program to help build a community of industry-ready data scientists.

Parameter 1: Course Content (Rating 4.7)

Comprehensiveness: Designed with optimal blend of rigour and relevance, it covers in-depth and industry-relevant content. Course comprises of real-world business case studies and live online lectures from industry practitioners. It covers Java programming, advanced excel, R, introduction to Python, data visualisation tools, big data technologies, business communication and others. They do not offer capstone projects and course is updated every 6 months.

Accessing students at the end of the course: Learners undergo online quizzes for every topic and online assessments for every module.

Learning resources: Downloadable resources, LMS, videos, question papers, industry webinars on relevant topics for improved understanding of application of learning

Parameter 2: Faculty (Rating 4.7)

Total no. of faculty members: 18

Total no. of faculty members with a PhD: 5

Total no. of faculty members with industry experience: 12

Student to faculty ratio: 10:1

Parameter 3: Student Experience (Rating 4.7)

Percentage of students who completed the course: 95

Post-completion engagement: Student get access to learning content and is invited as alumni for all learning webinars, meet-ups and other activities

Placement Assistance: None

Parameter 4: OtherAttributes (Rating 4.6)

Entry criteria for students: Entrance exam

External collaboration: Manipal Academy of Higher Education, Equifax, Gramener

The overall rating is 4.67

4. PG Program in Data Science By UpGrad In Association With IIITB

Founded: 2015

Mode of delivery: online

Duration of the course: 11 months

Number of hours: 500

Cities of Operation: UpGrad is an online learning platform

Course Fees: ₹ 2,35,000

Founded by media stalwart Ronnie Screwvala, UpGrad is an online higher education platform that provides rigorous industry-relevant programs designed and delivered in collaboration with renowned faculty and industry experts.

Parameter 1: Course Content (Rating 4.6)

Comprehensiveness: The curriculum has been designed along with IIIT Bangalore and multiple industry leaders. The broad areas of focus are data management (Excel, Python, SQL, Tableau), statistical and exploratory data analysis, machine learning, big data analytics (Hive, Spark, Sqoop). The candidates select specialisation of their choice in BFS, ecommerce, retail or healthcare. The course includes capstone project of 1-3 months. The course is updated every 6 months.

Accessing students at the end of the course: Students are assessed with the help of objective questions, individual assignments, group case studies with personalised feedback by industry experts. Participation in class, online discussion forums and online proctored exams contribute to overall CGPA of the learner.

Learning resources: Downloadable resources, LMS, videos, question papers, online virtual labs for Big Data

Parameter 2: Faculty (Rating 4.5)

Total no. of faculty members: 18

Total no. of faculty members with a PhD: 12

Total no. of faculty members with industry experience: 17

Student to faculty ratio: 10:1

Parameter 3: Student Experience (Rating 4.6)

Percentage of students who completed the course: 84%

Post-completion engagement: Career support guidance for one year after course completion, access to new and re-developed content, alumni network, opportunity to mentor subsequent cohort learners.

Placement assistance: Assistance is provided through alumni network, resume designing, helping them with interview process, 1-1 mentoring by industry expert, placement drives, help identify correct job profile, resources for interview and test preparation, and others.

Parameter 4: Other Attributes (Rating 4.5)

Entry criteria for students: Entrance exam

External collaboration: The program has flagship partnership with Uber, Genpact, Fractal Analytics and Gramener. They have collaboration with 30+ analytics industry experts from leading corporations and 250+ recruitment partners such as KPMG, Tech Mahindra, Zivame and others.

The overall rating is 4.55

5. Executive Program In Business Analytics (EPBA) By Jigsaw Academy In Association With SDA Bocconi

Founded:2011

Mode of delivery: Blended

Duration of the course: 10 months

Number of hours: 650

Cities of Operation: Online, global

Course Fees: ₹ 4.9 lakhs + taxes

Headquartered in Bengaluru, it was founded by the duo of Gaurav Vohra and Sarita Digumarti, and funded by Manipal Global Education Services (MaGE). It trains professionals in the areas of Analytics, Data Science, Big Data, Machine Learning, Business Analytics, and more recently, the Internet of Things (IoT).

Parameter 1: Course Content (Rating 4.5)

Comprehensiveness: The EPBA has the most comprehensive curriculum in Data Science &, Big Data in India. Along with SDA Bocconi, it provides unique and exhaustive course which includes analytics (R, Python, SAS), Big Data (Hadoop, Pig, Hive, Sqoop, Flume, Spark and Storm), Visualization (Tableau, Power BI).  It also covers descriptive analytics, predictive analytics, machine learning, an introduction to neural networks. It involves capstone project of more than 3 months, with course updation in every 6 months.

Access students at the end of the course: Graded assignments, capstone project and viva interviews

Learning resources: Downloadable resources, LMS, videos, question papers, case studies & online content

Parameter 2: Faculty (Rating 4.5)

Total no. of faculty members: 40

Total no. of faculty members with a PhD: 25

Total no. of faculty members with industry experience: 40

Student to faculty ratio: 1:2

Parameter 3: Student Experience (Rating 4.4)

Percentage of students who completed the course: 95

Post-completion engagement: They are a part of Bocconi Alumni Network, Guest Lectures for future batches & Jigsaw Mentorship Program

Placement assistance: Full assistance through alumni network, resume designing, helping them with interview process

Parameter 4: Other Attributes (Rating 4.4)

Entry criteria for students: Application + Interview

External collaboration: Collaboration with SDA Bocconi, Milan. Industry collaboration with Axteria, Analytics Edge, Tata Motors, Smart Cube, Data Semantics, and more for projects and hiring.

The overall rating is 4.45

6. Postgraduate Certificate in Business Analytics for Management Decisions (PGCBAMD) By XLRI Xavier Institute of Management

Founded:1949

Mode of delivery: Blended

Duration of the course: 12 months

Number of hours: 300

Cities of Operation: Jamshedpur

Course Fees: ₹  2,60,000 +GST

XLRI has visualised itself to be a partner in the liberation and development journey of the independent India. Over many years XLRI has developed its own identity and is one of the most premier institutes.

Parameter 1: Course Content (Rating 4.3)

Comprehensiveness: One of the most comprehensive programs in analytics, it offers 13 core courses in four broad areas of descriptive analytics, predictive analytics, prescriptive analytics and application based courses. It provides hands-on in tools such as R, Excel, Python. Students can also opt for specialisation certificate apart from the main PGCBAMD certificate, which could be in finance, marketing, operations, HR. They do not have capstone projects. The course is updated every year.

Access students at the end of the course: The assessments are done based on assignments, quizzes, midterm and end terms exams. Attendance and class participation are also used as a criterion by some faculties.

Learning resources: Books, downloadable resources, LMS, question papers, study materials in soft copy format like PDF, ppt etc.

Parameter 2: Faculty (Rating 4.4)

Total no. of faculty members: 18

Total no. of faculty members with a PhD: 16

Total no. of faculty members with industry experience: 18

Student to faculty ratio: 36:1

Parameter 3: Student Experience (Rating 4.3)

Percentage of students who completed the course: 95

Placement assistance: None

Parameter 4: Other Attributes (Rating 4.3)

Entry criteria for students: Based on eligibility criteria and personal Interview

External collaboration: Apart from the regular faculties of XLRI, the program also has instructors who are either industry experts or are part of some other esteemed institutes like XIMB, TAPMI etc.

The overall rating is 4.32

7. PGDM/MBA in Business Analytics By REVA Academy for Corporate Excellence, REVA University

Founded: 2012

Mode of delivery: Blended

Duration of the course: PGDM – One year, MBA –  Two years

Number of hours: 960

Cities of Operation: Bangalore

Course Fees: PGDM- ₹ 3.2 Lakhs, MBA- ₹ 4.2 lakhs

RACE is an initiative of REVA University, created to develop visionary enterprise leaders for corporates. RACE offers a range of specialised, techno-functional programs specially designed to suit the needs of working professionals to enhance their careers. The flagship program in Business Analytics is well recognized by the industry.

Parameter 1: Course Content (Rating 4.4 )

Comprehensiveness: The program designed with IBM provides hands-on training on relevant software tools and business analytics framework across industries. The program covers descriptive, predictive, prescriptive and cognitive analytics in real-life scenarios. The participants work on 15 plus tools including Python, R, TensorFlow, Keras, Advanced MS-Excel, Rapidminer, Tableau, QlikView, IBM Cognos and others. They have capstone projects of more than 3 months. The course is updated every 6 months.

Access students at the end of the course: This program follows a continuous assessment and grading pattern. Evaluations are done both internally and externally. Internal assessment is based on in-class quiz, discussions, participation in the discussion, while external is based on off-campus projects.

Learning resources: Books, downloadable resources, LMS, videos, question papers, online repository of 3,000+ electronic journals, access to IEL online database.

Parameter 2: Faculty (Rating 4.3 )

Total no. of faculty members: 14

Total no. of faculty members with a PhD: 4

Total no. of faculty members with industry experience: 14

Student to faculty ratio: 4:1

Parameter 3: Student Experience (Rating 4.2 )

Percentage of students who completed the course: 90

Post-completion engagement: All the participants get to enjoy lifelong access to the LMS, job portal, hackathons, information seminars etc. They also get to write blogs, white papers and research articles in various conferences and for RACE online media.

Placement assistance: Assistance through alumni network, resume designing, helping them with interview process.

Parameter 4: Other Attributes (Rating 4.2 )

Entry criteria for students: Entrance exam and industry experience of a minimum of 4 years.

External collaboration: The program is delivered in association with IBM. Other delivery and knowledge partners include Predictive Analytics Pvt. Ltd,  Jigsaw Academy.

The overall rating is 4.27

8. Executive Program in Big Data & Machine Learning  By NMIMS Global Access School For Continuing Education

Founded: 1994

Mode of delivery: Online

Duration of the course: 9 months

Number of hours: 120 hours

Cities of Operation: Presence across 22 cities with 75+ Authorised Enrolment Partners in India

Course Fees: ₹ 1,10,000

NGASCE is India’s leading management university trusted by 35,000+ students and over 5,000 alumni members are recognised across 2,500 corporates. The programs are designed to suits requirements of working professional and provide features such as learning on the go with mobile app.

Parameter 1: Course Content (Rating 4.2 )

Comprehensiveness: The big data and machine learning executive program is powered by SAS. It covers foundation course (business statistics, basic analytics tools and concepts like predictive, descriptive analytics), ANOVA, regression analytics, logistic regression, decision trees, cluster analysis, neural networks and others. They do not offer capstone projects, and course is updated every 6 months.

Access students at the end of the course: Proctored exams conducted at designated exam centres for each module with minimum passing percentage of 50. For practical learning, students are asked to present a case analysis in their respective specialisation.

Learning resources: Books, downloadable resources, LMS, videos, question papers, 24/7 software access via AWS.

Parameter 2: Faculty (Rating 4.3 )

Total no. of faculty members: 10

Total no. of faculty members with a PhD: 5

Total no. of faculty members with industry experience: 10

Student to faculty ratio: 1:3

Parameter 3: Student Experience (Rating 4.1 )

Percentage of students who completed the course: 60%

Post-completion engagement: Students are provided with a certified e-badge that can be tagged on professional networks such as LinkedIn and other social media pages.

Placement assistance: Partial assistance through alumni network

Parameter 4: Other Attributes (Rating 4.1 )

Entry criteria for students: STEM background required

External collaboration: This program is designed in collaboration with SAS.

The overall rating is 4.17

9. Post Graduate Executive Certificate Program (PGEP) in Data Science & Big Data By IMT

Founded: 1980

Mode of delivery: Blended

Duration of the course: 6 months

Cities of Operation: Ghaziabad

Course Fees: ₹ 80,000 + GST*

IMT Ghaziabad is a fully autonomous university and offers several postgraduate, doctorate and executive education programmes in management.

Parameter 1: Course Content (Rating 4.1 )

Comprehensiveness: The curriculum combines academic excellence and industry relevance to facilitate the participants learn analytics and big data tools (Apache), followed by in depth and advance level of statistical and quantitative. Students are given hand-on exposure to tools such as R, SAS, Python, Hive, Cloudera, data visualisation tools, and others. It includes capstone projects with top analytics companies. The course is updated every year.

Accessing students at the end of the course: Continued evaluation of students throughout the course.

Learning resources: Downloadable material, access to analytics labs during tenure of course

Parameter 2: Faculty (Rating 4.0 )

98% of in-house faculties having PhD.

Parameter 3: Student Experience (Rating 4.1)

Percentage of students who completed the course: 90%

Post-completion engagement: Candidates get IMT MDP alumni status. ICDM, a data management conference is organised by the institute.

Placement assistance: Students opting for this course are provided with placement support.

Parameter 4: Other Attributes (Rating 4.0)

Entry criteria for students: Industry experience

External collaboration: IMT has many industry associations and international collaborations. IMT has 50+ International collaborations towards various academic modules co-teaching and joint learning.

The overall rating is 4.05

10. Post Graduate Certificate in Business Analytics & Big Data By IFMR

Founded: 1970

Mode of delivery: Classroom

Duration of the course: 10 months

Number of hours: 266

Cities of Operation: Chennai and Sricity

Course Fees: ₹ 2.5 Lakhs

IFMR has been successfully offering PhD programs, MBA programs and executive programs for more than 2 decades now. It was established in 1970 as a not-for-profit society and has been sponsored by ICICI, the House of Kotharis and other major industrial groups. The board includes experts from Yale University, IISC Bangalore, and others.

Parameter 1: Course Content (Rating 4.0)

Comprehensiveness: Curriculum encompasses three important dimensions— mathematics & statistics, programming and application domains. It includes programming skills such as R and Python, predictive analytics, data mining and machine learning. Participants are provided hands-on sessions on SPSS Statistics, SPSS Modeler etc. It includes capstone projects of 1-3 months and course is updated every year.

Accessing students at the end of the course: Participants have to take a written examination after the completion of each module, which is evaluated by faculties. Candidates also have to submit POC during the last 3 months, which is also evaluated.

Learning resources: Books, downloadable resources, videos, question papers, class notes, powerpoint slides

Parameter 2: Faculty (Rating 4.1)

Total no. of faculty members: 16

Total no. of faculty members with a PhD: 7

Total no. of faculty members with industry experience: 11

Student to faculty ratio: 1:1

Parameter 3: Student Experience (Rating 3.9)

Percentage of students who completed the course: 90

Post-completion engagement: Alumni group is set up for continued engagement

Placement assistance: Partial assistance through alumni network, resume designing, helping them with interview process, setting up interviews for the participants

Parameter 4: Other Attributes (Rating 3.9)

Entry criteria for students: Industry experience required

External collaboration: We have a collaboration with IBM for joint certification for the program. The IBM collaboration gives us specific expertise in some industry domains such as BFSI, Telecom, Health care and manufacturing.

The overall rating is 3.98

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The post Top 10 Executive Data Science Courses in India – Ranking 2018 appeared first on Analytics India Magazine.

Analytics India Magazine conducted an independent study to understand how Southeast Asia has become an analytics enabler and is winning the race when it comes to growth and innovation in analytics ecosystem. With the region being pegged as the next growth engine, Asian economies have become the bedrock of growth and disruption.

Outside North America, the major hub for global AI development is now Singapore that has made the greatest advances in analytics and artificial intelligence. However, there are other countries like Malaysia and Vietnam that are also driving positive outcomes with AI adoption and in the last five years, have built a healthy ecosystem of deep tech startups.

The upside in analytics adoption doesn’t come as surprise, especially for countries like Singapore that have taken a substantial lead, thanks to the continued Government support seen in the form of e-Singapore government initiatives. The city state has innovated, tested and piloted several emerging technology solutions. In addition to this, Singapore, known as the global hub for innovation has made its biggest impact on the fintech ecosystem, thanks to its entrenched data and analytics practice. Singapore has made great leaps –from analytics engagement to analytics enabler, the country now contributes 44% to the analytics market size among Southeast Asia countries.

In South East Asia, the analytics & data science industry is estimated to be $6.8 Billion in market size. This is the current size of the industry and the potential size can actually be much higher. In addition to being a key driving force, Southeast Asia has also demonstrated research excellence in emerging technologies like AI. An independent study cited that more than 24,000 papers on artificial intelligence were published in Southeast Asia, with Singapore, Malaysia and Thailand accounting for 86% of the output.

The countries considered for this study are Singapore, Philippines, Indonesia, Malaysia, Thailand & Vietnam.

There are a few factors that could have led to the analytics adoption and innovation. Some of the core reasons are:

1. Developing analytics talent and supporting talent development programmes
2. Developing regional policy frameworks to support analytics development across sectors
3. Analytics adoption has increased thanks to the growth in data, which has led to the development of digital economy

Key Trends

• • The Analytics & Data science Industry in South East Asia is currently estimated to be $6.8 Billion (annual) in market size. This is the current size of the industry, the potential size can actually be much higher.
  • Singapore contributes 44% to the Analytics market size among Southeast Asia countries.
  • Malaysia contributes 23% to the analytics market
  • Philippines ranks a third with 11% contribution to the analytics market
  • Singapore is the leading country with the highest number of AI-related research papers and patents published in the last 10 years. Malaysia ranks second in the list of AI-related research papers published in Southeast Asia

Sector Type

The rapid shift towards digital has led to the rise in fintech boom and digital banking in Southeast Asia with the financial sector emerging as the biggest adopter of analytics and AI solutions. Southeast Asia is witnessing a fintech boom with a number of companies operating in the payments sector business. Major Southeast financial institutions are already providing mobile payments services.  The other key players in the fintech business in the region, in addition to the financial institutions include a slew of startups. A key reason why the region is leading in financial analytics adoptions is that many Southeast Asian countries offer an environment in which fintech start-ups can operate effectively.

• In terms of Sector type, finance & banking is the largest sector being served by analytics in South East Asia. Overall, 35% or $2.4 billion in market size to analytics industry comes from finance & banking
• Marketing & advertising comes second at 26%, followed by e-commerce sector generating 16% of analytics revenues in South East Asia.
• Telecom sector comes fourth generating about 8% revenue in analytics

Analytics Companies in SouthEast Asia

The adoption of analytics among Southeast Asian countries is advancing with companies adopting an enterprise-wide approach to analytics adoption. Adoption of analytics is highest among countries like Singapore, Malaysia and Philippines that have established analytics capabilities in place. Singapore leads in analytics adoption with companies having ingrained data-driven capabilities in their fabric and processes with 50% of analytics providers based out of Singapore. Adoption is also on the rise in Malaysia and Philippines with companies using analytics across businesses.

• More than 2,400 companies in SouthEast Asia claim to work on analytics in some form
• Almost 79% of these organizations have less than 50 employees
• On average, Analytics organizations in SouthEast Asia have 188 employees on their payroll
• Singapore houses almost 50% of all analytics companies in South East Asia
• It is followed by Malaysia at 17% and Philippines at 12%

Analytics Professionals in Southeast Asia

Even though emerging economies are now producing analytics and AI talent in far greater numbers than other developed region like North America, talent shortage still persists. The shortage in supply of analytics professionals makes it important for analytics users to develop comprehensive strategies for building, hiring or sourcing their required analytics resources. Leading enterprises are now building up internal analytics teams to fill the immediate skills and capabilities gap.

• Currently, there are approximately 150 thousand analytics professionals in Southeast Asia
• Singapore has the highest number of professionals in Southeast Asia at 38% followed by Philippines at 21%
• Even though Philippines contributes 21% in terms of manpower, its contribution in market size is just 11%
• 10 top analytics recruiters in Southeast Asia are Standard Chartered Bank, Google, Grab, Lazada Group, Shopee, Facebook, GOJEK, Agoda, Amazon Web Services & LinkedIn

Work Experience of Analytics Professionals

Southeast Asian countries are winning the talent war through innovative hiring strategies like hackathons and upskilling programmes. Even though the region faces a talent crunch in the area of analytics and AI, the average work experience of analytics professional is 9 years. Companies also grapple with a high analytics employee turnover, due to which they are not unable to build a deep analytics talent bench.  

• The average work experience of analytics professionals in Southeast Asia is 9 years.
• Around 4,100 freshers were added to the analytics workforce in Southeast Asia last year
• Almost 32% of analytics professionals in Southeast Asia have a work experience of fewer than 5 years
• 37% of analytics professionals have more than 10 years of work experience

Upsurge in Analytics Jobs was seen in the last couple of years

The unprecedented pace of growth in the analytics ecosystem in Asia means that attracting, developing, and retaining top talent is a key challenge. As compared to other developed regions, majority of analytics professionals in Southeast Asia transitioned only in the last 2 years.

• On average, analytics professionals in Southeast Asia have joined/transitioned to their current role in the last 3 years
• 55% analytics professionals in Southeast Asia have joined/transitioned to their current role in the last 2 years

Conclusion

Analytics adoption has gained substantial traction in Southeast Asia and countries like Singapore have now emerged as a hub for analytics, thanks to its rich talent base. In fact, the biggest impact is seen across sectors like banking, healthcare, manufacturing and healthcare and investments in analytics and cognitive technologies in Southeast Asia has also increased in the last five years. While Singapore emerged as a technology leader with close to 50% companies based there, companies in Malaysia and Philippines are also now leveraging advanced analytics to drive value. Analytics has emerged as the engine to drive businesses forward, however lack of analytics talent will remain the key issue in mainstream adoption.

Research Methodology

The findings contained in this report are based on surveys conducted among 100+  enterprise-level companies. The survey was circulated to find and gauge analytics capability within organisations. The survey was conducted among organizations across the sectors — BFSI, e-commerce, retail, telecom and manufacturing.  The data contained is a result of 6 months of research and by evaluating the findings, we aim to help global leaders understand the data science ecosystem in Southeast Asia and how the region has made great leaps in analytics innovation.

The post Study: State of Analytics in South East Asia 2019 appeared first on Analytics India Magazine.

Analytics India Magazine, a leader in providing the most trusted insights on the data science and analytics ecosystem in India announces The MachineCon 2019 to be held on May 24, Mumbai and May 31 in Singapore. The second edition of The Machine Con 2019 is expected to be most influential data science conferences in Asia, attracting over 400+ attendees and bringing some of the biggest Asian leaders on a single platform. A key highlight of the event is Analytics100 Awards — recognising the contributions of Asia’s leading 100 technology visionaries and the most respected analytics practitioners who have advanced data and analytics initiatives in their organisation.

MachineCon is produced by the team behind Cypher — India’s largest Big Data and AI summit that has become the meeting point for technology leaders and innovators from the field of AI and data science.

MachineCon will bring 100+ technology leaders from leading tech enterprises of Southeast Asia to discuss how some of the most successful Asian senior executives are handling digital transformation through emerging technologies. The event will connect technology solution providers, enablers and potential adopters of AI and machine learning solutions. Technology heads across Asia will get to hear about the latest global trends in artificial intelligence and will gain insights from discussions with their peers during the networking sessions. The summit will also provide actionable insights to senior management and executives through a mix of insightful presentations and case studies.

What’s more, the second edition of MachineCon 2019 provides analytics leaders and CIOs the opportunity to reach out to a diverse audience and meet with potential adopters in two regions  — India & Singapore.  Billed as the most “diverse tech summit”, MachineCon will feature 50+ speakers who will share their insights on the best practices in AI, machine learning and emerging technologies. MachineCon provides delegates with a unique opportunity to

interact with Asian thought leaders and gain cutting-edge insights from a series of presentations and case studies hosted by international technology leaders.

Talking about MachineCon 2019, Bhaskar Gupta, Founder & CEO, Analytics India Magazine shared, “I have been watching the analytics ecosystem evolve close to a decade now and for the last 10 years our mission has been to provide the tools and resources to help senior executives, entrepreneurs and startup founders turbocharge their analytics practice to the next level. The tech ecosystem in Asia is the fastest growing in the region with analytics. Keeping this mission in mind, we are now reaching out to Asian analytics thought leaders who can share their insights and talk about their digital transformation journey”.

Analytics100 Awards Will Honour Asia’s 100 Leading Analytics Leaders

This year, at MachineCon 2019, the stage is set for a bigger analytics awards – Analytics100 Awards, which will recognize the contributions of 100 leading analytics visionaries from across Asia. Analytics100 will acknowledge the AI and machine learning innovators behind the success of Data Science ecosystem in India & Southeast Asia who have redesigned the technology landscape.

Talking about Analytics100 Awards, Gupta said, “MachineCon will bring over 100 business leaders and data scientists from across Asia and honour their achievements and contributions to the analytics ecosystem. These technology leaders have supported the growth and development of analytics in their organisations and have created profitable solutions”.

Who Should Attend

Attend the groundbreaking summit, driven by powerful discussions and join over 150+ Data Science technology leaders who will examine the challenges and opportunities that lie ahead in the AI-driven age. CIOs, CTOs, VPs and Head of Data Science teams who are building their analytics practice can gain valuable insights from this two-day summit held in Mumbai and Singapore.

This is an invite-only conference. Join us in Mumbai and Singapore for an insightful and informative two-day event and network with some of the biggest names from Asian analytics industry. 

When: May 24, 2019 at Novotel Juhu, Mumbai & May 31, 2019 at Novotel Clarke Quay, Singapore

To know more, click here

The post Meet Asian Analytics Leaders At MachineCon 2019, Asia’s Most influential Analytics Summit To Be Hosted In India & Singapore appeared first on Analytics India Magazine.

In our annual study with Great Learning, we examine the landscape of job requiring data science and analytics competencies and skills. Our market snapshot gives a quick view of the evolving talent market and highlights how employees need to develop a blend of skills to strive ahead in data science roles. The report clearly demonstrates while skills in Python and Java are highly sought-after, a professional programmer or a data analyst should have the ability to learn new coding languages. It also highlights the need for work-based learning opportunities which will help employees gain the necessary skills. Many companies have mentor-style programmes to help employees understand and upgrade the skill-set needed at the workplace.

The report seeks to provide forward-looking insights for recruiters and hiring companies to build a detailed understanding of their talent needs, counter the skills gap in their workforce strategy and build a talent pipeline and also highlight the talent hotspots in India. The research presented in this report has been collected through a mix of analysis from publicly available data and other information sources.

Top Trends In Data Science Jobs

1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.

2) Forecasting annual growth in data science professionals in 2018: Compared to the numbers in 2017, last year had an optimistic job growth with a 45% increase in open job requirements

3) Increase in analytics jobs offering more than 15 lakh per annum:There  has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared t0 2017.

4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom

5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability

6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities

7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers

Highlights

• While, it is difficult to ascertain the exact number of open analytics job openings; according to our estimates, close to 97,000 positions related to analytics & data science are currently available to be filled in India.
• This is almost 45% jump in the open job requirements, compared to same time a year back.
• Compared to worldwide estimates, India contributes 6% of open job openings currently. Growth in the number of data science jobs globally was much higher than India
• Last year India contributed 10% of worldwide open job requirements which has decreased to 6% this year, even though there have been an overall growth in numbers
• 10 leading organisations with the most number of analytics openings this year are – Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte
• Almost 97% of analytics jobs advertised in India are of full-time basis. Just 3% form the part-time, internship or contractual jobs
• Top designations advertised are: Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultant, Statistical Analyst, Hadoop Developer

Analytics Jobs By Cities

• In terms of cities, Bengaluru accounts for around 24% of analytics jobs in India. This is a decrease from 27% a year ago
• Delhi/ NCR came second contributing 22% analytics jobs in India, up slightly from 21% a year ago
• Approximately 15% of analytics jobs are from Mumbai. This is significantly up from 12%, last year
• Tier-2 cities have also contributed to the growth of analytics jobs with numbers rising from 5% in 2016 to 7% in 2017. There was a steady increase until 2018 but the numbers dropped to 10% in 2019

Analytics Jobs By Industry

• The Banking & Financial sector continues to have the biggest requirements for analytics professionals. 38% of all jobs posted for analytics were from the banking sector. There is a dip in hiring BFSI sector as compared to 2017 which accounted for 41% job openings
• Around 12% of analytics jobs were in the e-commerce sector as opposed to 8% in 2018, 10% in 2017 and 14% in 2016
• The Telecom industry saw the biggest fall in number of analytics jobs advertised. Just 4% of analytics jobs in India are from the Telecom sector as opposed to 8% last year

Experience Requirement For Analytics Jobs

• There has been an overall shift towards hiring for entry-level roles in analytics in 2018
• Around 70% of analytics openings are for candidates with less than 5 years of experience
• 21% analytics jobs posted are for freshers and this number has risen to 17% from last year
• 31% analytics job openings are for professionals with more than 5 years of work experience. There is a dip in hiring at mid-stage level as 2017 reported a 38% job postings for professionals with 5+ years of experience

Analytics Jobs Across Tools/Skills

• Python continues to reign as the tool of choice, thanks to its versatility. As per our research, the demand for Python professionals is the highest among all analytics recruiters
• Almost 17% of all advertised analytics jobs in India demand for Python as a core skill whereas 16% demand Java
• R skills comes third in the most critical skills required with 10% of all analytics jobs looking for R professionals
• Among BI tools, Tableau skills continue to be most in demand, followed by Microsoft Power BI & Qlikview
• Among cloud solutions, analytics recruiters demand AWS as the most preferred skill, followed by Azure
• Among Big data tools, Hadoop skills are most in demand, followed by Spark
• SQL continues to be the most popular database platform among analytics recruiters followed by NoSQL and MongoDB
• This year we also looked at which statistical technique do analytics recruiters demand the most. Segmentation is the most in demand skill followed by clustering and classification

Analytics Jobs By Salaries

• The median salaries being offered by advertised analytics jobs in India is INR 11.5 Lakh/ annum
• Advertised salaries tend to be lower than actual salaries. In our earlier report, we indicated the median salaries of analytics professionals in India to be 12.7 Lakh
• There  has been an increase in the numbers of analytics jobs offering more than 15 Lakh annual salary.23% of all advertised jobs last year were offering more than 15 Lakhs as opposed to 20% a year before
• 25% of all analytics jobs offer a salary range of 6-10 lakh, followed by 22% for 3-6 lakh annually
• Almost 60% of all advertised analytics jobs in India offer a salary of less than 10 Lakh per annum

Analytics Jobs Across Company Type

• Almost 60% of all demand for analytics professionals is from the MNC IT & KPO Service Providers in India
• Just a year ago, captive centres in India used to have the most number of analytics job requirements in India. Their contributions to open jobs have reduced significantly this year
• 13% of all analytics jobs advertised are by Domestic IT & KPO service providers, down from 21% a year ago
• 4% of analytics jobs advertised are by Indian companies that require analytics for internal consumption

Broader Trends Affecting Data Science Areas

Scaling up skilling: As more talent gets concentrated in metro areas, recruiters will tap into this market to hire talent. In addition to this, large diverse metro areas will become the hub for educational initiatives undertaken by internal stakeholders and external organisations.

Acqui-hiring is big in Data Science: With the continued lack of talent plaguing the technology sector in India, the acqui-hiring trend has taken off in popularity in India and we are seeing big tech firms snapping up data science consulting firms in a bid to  bolster their bench strength.

Workforce to create new forms of value: In addition to this, companies are increasingly looking inwards to identify whether they have the talent to capitalize on new  opportunities that can reshape the nature of industry.

Workforce prioritises skill development: On the other end of the spectrum are employees who are on a path for lifelong learning approach to skills development. As leading institutes respond to the rising demand for analytics skills with tailored programs, skills development programs will play a critical role in updating the analytics capabilities and preparing the workforce for new roles.

Traditional Indian IT giants are starting accelerators/incubators: Over the last few years, we have seen traditional Indian IT companies spawning new divisions or accelerators to develop a new product/service stream outside the main organisation. In a bid to pivot to newer tech domains and capitalize on emerging technologies, companies are diversifying their portfolio by building new teams and setting up new divisions. Case in point is Reliance Jio that is building a team to work on machine learning and AI. With the advantage of being backed by larger corporations, these entities are able to attract talent.

Conclusion

As analytics as a discipline becomes more entrenched across domains, especially in India’s financial services sector, organisations and high-growth startups are on a hiring spree. However, the market continues to be employer-driven and finding the right talent is a growing challenge for organisations and startups. On the skill side, as more companies bolster their analytics capabilities, they are aggressively hiring professionals with sound technical knowledge. A key takeaway is that the analytics and data science talent supply will not be able to keep pace with job growth. In other words, there isn’t enough analytics talent and organisations will continue to face hiring challenges.

The post Study: Analytics And Data Science Jobs In India 2019 – By Great Learning & AIM appeared first on Analytics India Magazine.

The field of information technology is evolving at a faster rate than usual. Areas like artificial intelligence, data science, data analytics and internet of things, among others, are now being integrated with industries across all sectors. Many times, it becomes difficult to keep a track of these technologies and keep up with them.

Now, more than ever, we are seeing a pattern where young professionals are finding themselves confused about their future in the company — be it regarding their job roles, their progress, or even the fear of losing their jobs to machines.

This month, Analytics India Magazine, in association with Jigsaw Academy, decided to find out what goes on behind the making of a good career graph in 2019. From the education background to upskilling strategies and organisational partnerships for further studies, today’s young professionals have found many ways to fight the career ennui.

In fact, industry numbers suggest that there is an 18% to 20% attrition rate for young professionals with around three to four years of work experience.  

About The Study

The samples were collected by asking respondents to fill in a survey created by AIM about what these young professionals felt like at work — professionally. This included various sub-topics such as growth opportunity in the current organisation, their need to upskill and their preference of a particular Emerging Tech.

We took opinions from young Indian professionals with work experience between 2 to 4 years to get a thorough idea of the working environment in this growing field. Our survey was met with much enthusiasm — and we got some great insights from it. Some of them were expected, and many of them were real eye-openers.

How long have you been working in this industry?

• Our core demographic for the survey, young professionals with work experience of between two to five years constituted a majority of the result at 39%
• The other important constituents to the survey were young working professionals with less than two years of work experience at 27%
• 19% of our respondents were persons with 5-10 years of work experience.

Do you wish to work in your current technology/domain for the next five years?

• The answer to this question was an ambitious “no” with 63% young professionals saying that they would definitely want to change their roles in terms of technology or domain.
• About 27% respondents were not sure about their future in their chosen sector
• And only 10% of the respondents said that they wanted to keep working in the same technology domain.

Do you feel you face enough growth opportunities in your current role?

• Interestingly enough, an overwhelming majority of the professionals at 66% said that they did not face enough growth opportunities in their current role.
• Only 34% said they did foresee growth opportunity.

If you do want to change your job/role, how do you plan to do it?

• When asked the question about how these young professionals wanted to take their career ahead if looking for a change, the majority of them, at 59%, said that they would prefer to upskill, while keeping their current jobs.
• 18% of the respondents said that they would quit working full time and take a break to pursue a subject properly.
• 13% of the respondents, on the other hand, said that they would like to change jobs, as that would also allow them to get more exposure and experience.

What Future Tech would you like to upskill to?

• When asked about what future tech would most of the young professionals upgrade their skill set to, most young professionals gave a very clear answer: artificial intelligence and machine learning-related technologies were most in demand at 44%.
• 35% of the respondents with 3-5 years of work experience in the IT sector also said that data science would be a great option to upskill.

If yes, how would you like to upskill?

• 37% of the respondents who prefer to upskill said that the best way to do this was through taking up a part-time educational programme.
• 35% said that they would prefer studying on their own, using their own material which is available freely online.
• 19% of the respondents said that they would like to quit their full-time jobs and pursue full-time education.

Do you think, your organisation should sponsor to upskill you?

• This question was met with a very positive reaction where 64% of the respondents said that they would like it if their organisations encouraged the culture of upskilling by sponsoring it.

Do you think your current job will be automated in the next 3-5 years?

• Probably reacting to the dystopian warnings predicted all around the country via different means, almost half the respondents, at 49%, said that they were worried that their job would be automated within the next five years.

How many times do you hear keywords like AI/ IoT/ Blockchain in your current organisation?

• New tech concepts such as AI, IoT and blockchain have become fairly common at workplaces today. Clearly, respondents have had positive experience towards it, because, 44% said that they heard these terms frequently.
• 28% respondents said that they heard these terms regularly.

Are you already looking out for a job in tech like AI/ data science/ IoT/ Blockchain?

• Clearly, after gaining a few years of work experience in the IT sector, the next step for most young professionals is to then move on to New Tech. This was demonstrated by an overwhelming majority of 72% who said that they were already looking out for a new job in this area.

Do you think your past experience would be wasted once you move to another technology?

Many professionals have expressed this fear in the past that while upskilling is a good move for the career, but it may sometimes undermind the past work experience.

• But 52% of the respondents said that they did not think that upskilling or moving into another, newer technology would waste their past work experience.
• However, 29% of the respondents were not sure about this

How much salary hike do you think you would receive once you transition to another tech?

Most of the time, these young professionals want to move to newer territories in the future tech is because of the growth opportunities and monetary compensation.

• 58% respondents think that they would get a salary hike between 20% to 50% after they upskill themselves or transition to new tech.
• 18% respondents said that they expected they would get as much as a 50% hike

The Managerial Perspective

Mohan Sitharam, the chief human resources officer at Subex, told Analytics India Magazine that they always preferred to upskill and retain current employees than hire outside talent. “Subex is a product company with deep expertise in the telecom domain. For a business such as ours, it is an obvious choice to retain and upskill our engineers. We consider re-training our existing resources as raising the bar of our talent’s competence, and in our experience, we realise that the odds of advantage are twice when we retrain our existing talent, rather hiring skills.”

Bhuvan Nijhawan, Director Education – Asia Pacific, SAS, told Analytics India Magazine, “Young Professionals these days are constantly looking to upskill themselves, given the rapid market demands and advancements in Data Science technologies. These employees not only intend to continue with their current employer but also upskill themselves for career progression and growth. To encourage such budding talent and recognise their skillset, SAS® has introduced SAS® Digital Badges. These badges are given only to those who take up our official Training programs or those who clear the Certification exam. This also acts as a reference point for potential interviewers.

With increasing demand for skills in Data Science, Machine Learning and Artificial intelligence, tech-savvy professionals with 0 to 3 years’ experience often look forward to acquiring these skillsets. Moreover, the hype in this field often leaves them with scattered information making it difficult to choose relevant programs to their profile. With this in consideration, SAS has developed assessment tools which map their existing skill sets thus aiding with recommended curricula relevant to their profile. We also work to connect these talents with our partner organisations and customers.”

Road To Career Transition

Abdul Baasit, one of the students who completed the Postgraduate Program in Data Science and Machine Learning from Jigsaw Academy has transitioned successfully in his career.

“I had a background in IT but in around 2015, I discovered my interest in data and generally working with mathematical solutions,” he said. Abdul initially completed the course in Data Science with SAS and while he did then move into the data science space, he felt that it wasn’t quite enough. “I’d noticed the potential of analytics in the job market and was keen to get further into it. I felt that doing another more in-depth course would be helpful,” he added.

Here’s the Complete Report

The post 3 Years In IT Sector, What Next: Study By AIM & Jigsaw Academy appeared first on Analytics India Magazine.

The field of information technology is evolving at a faster rate than usual. Areas like artificial intelligence, data science, data analytics and internet of things, among others, are now being integrated with industries across all sectors. Many times, it becomes difficult to keep track of these technologies and keep up.

Now, more than ever, we are seeing a pattern where young professionals wanting more challenging jobs and more interesting work, especially in the Emerging Tech sector. And hackathons have turned out to be a very valuable source of answer to both employees as well as employers.

This month, Analytics India Magazine decided to find out how employers and big organisations are using this nifty platform called hackathons to get in touch with the talented people who can be absorbed into the company, and people who can solve unique problems.

About The Study

The samples were collected by asking respondents to fill in a survey created by AIM about what employees, as well as employers, thought about hackathons as a tool for hiring as well collaborate productively for different projects. This included various sub-topics such as growth opportunity in the current organisation, collaborative spirit of hackathons and the rate of success for both the parties.

We took opinions from young Indian professionals as well as senior employers to get a thorough idea of the working environment in this growing field. Our survey was met with much enthusiasm — and we got some great insights from it. Some of them were expected, and many of them were real eye-openers.

Has your organisation ever conducted a hackathon?

How frequently does your company conduct hackathons?

Our core demographic for the survey had a very clear answer to the above question. 44% of the respondents said that their organisations conducted hackathons only once a year.

Why do you think your organisation conducts hackathons?

Interestingly, we found out that a majority or 32% of the organisations use hackathons as a great way to crowdsource solutions for problems.

• We also found out that 16% of the organisations conducting hackathons use it for hiring.
• 16% use it for organisational branding both in and out of the campus.

How reliable are hackathons for assessing baseline skills in candidates and potential employees?

Who do you usually prefer conducting hackathons with?

For this question, we found out that most of our respondents, 40%, preferred to collaborate with external agencies to get the most out of their hackathons.

What kind of hackathons do you prefer?

When asked what kind of format the organisations of the employers preferred to conduct the hackathon, a majority, 56% of them replied with fact that they preferred with the hybrid format, which is a mix of online and offline format.

Among the following, what do you find most disagreeable about hackathons?

Which one do you think hackathons should focus more on?

When asked about the focus of the hackathons the organisations as well the participants responded with:

• 44% of the people said that developing realistic working prototypes and solutions for the challenges presented was crucial.
• 28% of the respondents said that hackathons should focus on inspiring participants to consider the “art of the possible,” to think up and imagine futuristic forward-thinking solutions with technologies that are not available or haven’t been created.
• 28% of the respondents said that they should focus on creating an atmosphere where people with different skill sets can brainstorm together and learn about a problem/challenge.

What challenges have you/your company faced while conducting a hackathon?

• 36% of the respondents said that one of the key challenges they faced during a hackathon was about curation of the problem statement and dataset.
• 24% of the respondents said that awareness of the ongoing event was a huge challenge.

Do you want your organisation to conduct a machine learning hackathon?

Hackathons have been in vogue for quite some time in the tech world to source innovative ideas. Many employers have also used this medium to brand themselves well with their prospective employees. For the participants, hackathons address one or many of these motivations – prize money, bragging rights, learning opportunities and/or hires. Quite often the hiring as a motive is understated in many hackathons. So when we finally the crucial question, 84% respondents replied with a resounding yes.

The post How Important Are Hackathons For Your Organisation: A Survey By AIM appeared first on Analytics India Magazine.

Over the last few years, financial institutions have achieved exponential growth, driving innovation in the sector by building enterprise-wide analytics capability, that is now woven into the key business processes throughout the organisation. Much of the growth in analytics jobs is also credited to the fintech boom in India – led by top digital payments firms Paytm and PhonePe that leverage data analytics to not only digitize cash payments but also scale the reach of their customers.

The emerging fintech ecosystem in India has created a slew of opportunities in data analytics and AI space. On the other end of the spectrum is tremendous innovations happening in the Financial Services (FS) organisations space with banks and financial institutions applying advanced analytics and machine learning across the entire business model. Today, banking and financial services industry has adopted analytics across three broad functional areas – customer satisfaction, risk management and operations analytics.

Our annual study done by Jigsaw Academy and AIM captures the data-heavy financial ecosystem in India and dives into the talent market — unpacking key trends across jobs, salaries, hiring trends, top financial companies in India hiring financial analytics talent and the AI talent divide across companies. The research also reveals what’s hampering the growth and innovation story is the big talent challenge facing financial institutions.

Key Trends

• The size of the analytics industry in the financial sector is currently estimated to be $1.2 billion (annual) in revenue
• Currently, there are approximately 60,000 data science and financial analytics professionals in India, working in the finance sector
• According to our research, 7,000 freshers were added to the financial analytics workforce in India in 2018
• Mumbai continues to be the financial hub with 33% analytics professionals working in the financial capital
• The median financial salary is India is INR 13.4 lakh across all experience level and skillset
• On average, the entry-level salary for financial analytics professionals is less than INR 6 lakh while 3% of financial analytics workforce takes home more than INR 50 lakh
• Women participation is skewed with only 27% of women making up the financial analytics workforce in India
• Top skills FIs look for are Data Analytics, Marketing Analytics, Predictive Modeling, Business Analytics, SAS, Campaign Management & Business Intelligence

Work Experience Of Financial Analytics Professionals in India

• The average work experience of Data Scientists in financial industry in India is 7.2 years, marginally lower than the overall average number of 7.9 years
• Around 7,000 freshers were added to the financial analytics workforce in India this year
• Almost 45% financial analytics professionals in India have a work experience of fewer than 5 years, same as last year
• 23% of financial analytics professionals have more than 10 years of work experience
• Women participation in financial analytics workforce remains low – only 27% of financial analytics professionals constitute the workforce

Tenure  Of  Analytics Professionals

• On average, financial analytics professionals in India have joined/transitioned to their current role in the last 3.1 years
• 61% financial analytics  professionals in India transitioned to their current role in the last 2 years
• Just 16% stay in the same role for more than 5 years

Leading  Cities  Hiring  Financial  Analytics Professionals

• Mumbai leads the cities in terms of the size of ecosystem. 33% of financial analytics professionals in India are working in Mumbai
• This is closely followed by Bangalore at 24%

Top Financial Analytics Companies in India

• The 10 companies that employ the maximum number of financial analytics professionals in India are HSBC, American Express, ICICI Bank, Moody’s Analytics Knowledge Services, Citi, JPMorgan Chase & Co., HDFC Bank, Axis Bank, EY & Barclays
• Close to 250 companies in India work on financial analytics in some form. This includes a small number of companies into products and a larger chunk offering either offshore, recruitment or training services
• Moreover, the number of vertical financial analytics companies in India is less as compared to North America and Asia Pacific. India accounts for just 7% of global Analytics companies

Company Size

• On average, Indian financial analytics companies have 340 employees on their payroll
• Almost 88% of financial analytics companies in India have less than 200 employees.

Analytics Talent Divide Across Companies

• Almost 59% of financial analytics professionals in India are employed with large-sized companies(with a total employee base of 10k+)
• Mid-size organizations (total employee base in the range of 200-10K) employ 30% of all financial analytics professionals in India
• Startups (less than 200 employee base) employees form 11% of financial analytics professionals in India
• A large percentage of financial analytics professionals are absorbed by large service providers or MNC captive units
• This is also a clear indication of the financial analytics talent divide between enterprises and startups in India, which will only continue to widen further

Financial Hubs in India

• 41% of all companies that work on financial analytics in India are based out of Mumbai, the financial capital of India
• This is followed by Bengaluru at 18%

Financial Analytics Jobs & Salaries in India

• The median financial analytics salary in India is INR 13.4 Lakhs across all experience level and skill sets.
• Around 36% of financial analytics professionals in India have an entry-level salary of less than 6 Lakh
• Almost 3% of financial analytics professionals in India command a salary higher than 50 lakh
• While it is difficult to ascertain the exact number of open financial analytics job openings, according to our estimates, close to 36,000 positions related to financial analytics are currently available to be filled in India
• 10 leading organizations with the highest number of financial analytics openings this year are – Fidelity Business, eClerx Services, Bajaj Allianz, Morgan Stanley, RBS India Development, HDFC Bank, Ernst & Young, Invesco, PwC & ICICI
• The top skill sets that financial analytics employers are looking for are Data Analytics, Marketing Analytics, Predictive Modeling, Business Analytics, SAS, Campaign Management & Business Intelligence

Financial Analytics Jobs By Cities

• In terms of cities, Mumbai accounts for around 28% of financial analytics jobs in India.
• Delhi/NCR comes very close second contributing 28% jobs in India.
• Approximately 21% of financial analytics jobs are from Bengaluru

Key Takeaways

Innovation Drivers in the Financial Ecosystem

• As banks and financial institutions transform to become data-driven enterprises, digital technology will soon become the backbone of FIs

• In order to build a sophisticated analytics capability, banks and FIs are now investing along several important dimensions – technology infrastructure, strengthening processes and people

• Another key area emerging is banks and major FIs are setting up dedicated analytics and AI CoEs in strategic partnerships with fintech and knowledge mentors to drive the innovation story forward

• Increasingly, the financial industry has started leveraging in-built analytics, machine learning and AI capabilities for specific use cases to drive profitability and growth

Rethinking Talent Strategies

• The key traits financial institutions look for when hiring talent are:  quantitative and technical skills as well business acumen to generate insights. The core capabilities required are ability to use statistics, quantitative analysis and information-modelling techniques to make business decisions
• There’s also a fundamental rethink in terms of people strategy with most FS organisations in India fostering a culture of innovative thinking and embracing hacks for hire to bolster their human capital.
• In order to meet the growing talent demand, financial organisations are investing in developing robust learning modules and skill development programmes to reskill staff
• Most leading financial organisations are now taking an active step and partnering with third-party institutions to train and source talent. This is in a way, is paving the way for talent exchanges

Corporate Training As A Key Indicator of Changing Trends in the Financial Sector

Way Forward

Judging the impact of artificial intelligence and automation on job roles and skill-set, and how it will redesign jobs frameworks, (for eg. AI’s robo-advisors have replaced financial advisors), FS organsiations are now doubling down on closing the talent gap by collaborating with leading institutes and stakeholders to even out the supply and demand gap. In an automated world, financial organisations have recognized the changing skills demand and in order to keep pace with the market, organisations are teaming up with stakeholders to develop tailored solutions for talent management and develop work-ready workforce. 

Here’s the complete Report

The post Analytics & Data Science In Indian Financial Sector – A Deep Dive 2019: By AIM & Jigsaw Academy appeared first on Analytics India Magazine.

The annual Analytics India Salary Study presented by AIM and AnalytixLabs, now in its fifth year, is the only study coming out of India that digs deeper into salary trends to give a comprehensive view at how analytics salaries have changed over the years, skills that are very much in-demand and the hiring challenges it could present. Our report looks at the distribution of average salaries across years of experience, job category, region, industry, education, gender and tools and skills. All in all, we expect to see another busy year for analytics hiring market.

Data Analytics professionals are currently benefitting from the big data wave with analytics professionals earning 26% higher than an average software engineer in India. Now, similar to last year, the average salary of analytics practitioners has remained steady across all experience levels with the remuneration remaining unchanged. However, the demand for mid-level professionals with median compensation of INR 6 lakh onwards has surged, demonstrating that analytics as a function in India Inc has become more mainstream with enterprises capitalizing on growth opportunities.

What we have observed is that over the last three years (2016 to 2018), there has been a pronounced growth in median salary across all experience levels, increasing from INR 9.5 lakh in 2016 to INR 11.7 lakh in 2017 and touching INR 12.7 lakh per annum in 2018. However, this trend has levelled off with the average analytics salary capped at 12.6 lakh per annum across all experience levels in 2019. A high point is that analytics salaries continue to exceed other software engineering roles with analytics professionals out-earning Java counterparts by almost 50% in India. The study also found a 1.8% increase in salaries of entry-level analytics professionals with experience between 0 to 3 years. It is also clear that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals. Our research also confirms the observation that organisations data scientists with a PhD and they also lean towards candidates from elite engineering institutions.

Key Trends From 2018-2019 are:

• The median analytics salary in India for the year ending 2018-19 remained steady at INR 12.6 Lacs across all experience levels and skill sets
• As compared to last year, median salaries for analytics and data scientists have remained steady since a year ago
• Over one third (36.2%) of analytics professionals take a salary of less than 6 Lakhs. This is lower than 2017 (39%) & 2018 (37.6%) which also implies that base salaries for entry-level professionals has gone up

• On the other hand, salaries at senior managerial levels have plateaued and dipped by one percent. There has been a one-percent decrease in professionals earning 25 lakh and onwards with the number coming down to 9.4% this year from 9.5% in 2018
• Mid-level analytics professionals who command considerable subject matter expertise are visibly more in demand with salary falling in the bracket of 6 lakh to 25 lakh per annum this year, thus keeping the median salaries stable

Salaries in Analytics Compared to IT Roles

• Analytics professionals earn 26% more than software engineers in India
• In case of other IT roles, the salary gap is higher, underscoring the advantage held by analytics professionals
• Analytics professionals out-earn Java programmers by almost 50%

Salary Trend Across Cities

Salaries in a city is a function of two factors- the demand for a skillset and the relative cost of living in that city. While Bangalore & NCR has the highest demand for analytics professionals, Mumbai is India’s most expensive city. Hence, Mumbai is the top earning location followed closely by Bangalore. The rest of India, Hyderabad & Chennai is much lower showing a marked distinction in salaries between cities.

• Mumbai continues to be the highest paymaster in Analytics at almost 13.7 Lakh per annum, followed by Bangalore at 13.0 L.akh
• Chennai & Hyderabad are the lowest paymaster at 10.7 Lakh & 10.3Lakh respectively.
• In the 0-6 Lacs bracket, Chennai dominates other cities with 40% of Analytics professionals earning below 6 Lacs followed closely by Hyderabad at 39%.
• Mumbai leads the pack in more than 15L salary bracket, with 28.5% earning within this bracket, followed closely by Bangalore at 26%
• Among the metros, Hyderabad saw the highest annual increase in median salaries for analytics professionals at almost 5% — from 10.2 Lakh in 2018 to 10.7 Lakh this year.
• In terms of salary hikes, Hyderabad was followed by Bangalore in annual increase in salaries at 4.1%
• Pune reported the lowest salary hike of  1.4%

Salary Trends Across Experience Levels

Salaries across experience levels is a critical factor to judge how the industry rewards seniority. Experience Levels could also be seen as designation like analyst, manager, director etc, but we chose to look at the number of years in the industry. This is a more quantitative assessment and can resonate easily with senior folks. With more and more enterprises setting up dedicated analytics practice, the number of high-profile appointments have gone up. Beefing up the senior leadership stack is not just limited to  enterprises but also high-growth startups like OYO and Swiggy that are hiring senior talent to architect a global  strategy and push growth. Those transitioning to upper management can expect a 60% jump in their salaries.

• Analytics salaries saw a slight increase at the entry level (0-3Yrs experience) this year – from 5.2L median last year to 5.3L per annum.
• At entry level, almost 76% of analytics professionals earn under  0-6 Lakh per annum
• The salaries for all other experience levels have relatively decreased as compared to 2017-2018
• A transition to senior analytics leaders, with more than 12 years of experience, can lead to almost 60% increase in salaries

Analytics Salaries Across Industries

Salaries within an industry is affected by broadly two indicators — the rate of adoption for that technology within that industry and the relative value that the industry garners from the technology. What we have observed is that more and more enterprises are morphing their traditional businesses into data driven companies to succeed in the competitive market.

A higher adoption rate would signify a relative stabilization of resources and processes which in turn would cause a dip in salaries. Industries that have recently adopted analytics would also see a skew towards higher senior level professionals vis-à-vis other industry, thus pushing the median salaries higher.

We have seen a marked increase in global corporations launching full-fledged analytics unit across a wider spread of sector. Among the industries that value Data Science the most is telecom that has the highest median salaries at 18 Lakhs, while Media & Entertainment vertical offers the least median salaries for analytics professionals at 10 Lakhs. This is a very wide range (higher variances) in terms of median salaries.

For eg: Banking sector was one of the earliest adopters of analytics in India. It is also by far the biggest recruiter of analytics professionals in India. The salaries in Banking for analytics professionals currently stands at the lower spectrum in median terms. Eventually, this salaries variance across industries should reduce as adoption increases over time.

• Telecom industry pays the highest median salaries to its analytics professionals with average compensation being  18 Lakhs
• Telecom industry also has the highest number of professionals (4.8%) commanding in the upper management with salaries in the range of 50 Lakh – 1Cr salary range
• Media/ entertainment industry pays analytics professionals the lowest with median salary of 10 Lakhs.
• Energy/ Utilities/ Manufacturing has the highest number of professionals with 1Cr+ salary range – 1.2% of analytics professionals in telecom sector command median salaries more than 1Cr
• The largest number of entry level analytics professionals are employed in Media/ Entertainment firms. Almost half of analytics professionals in Media/ Entertainment firms command less than 6 Lakhs salary
• E-commerce sector has the highest increase in the salaries of analytics professionals, at 4% –  from 11.2% in 2018 to 11.7L this year.
• Media/ Entertainment has highest decrease in the salaries of analytics professionals, at 4.2% –  from 10.3% in 2018 to 9.9L this year.

Salaries Across Data Science Roles

One of the most critical factors for the increase in salary expectations for an analytics professional would be an advancement of skillset. With companies moving data to the cloud, big data tools like Spark, Hadoop and Scala are becoming more popular. This requires candidates with experience with big data platforms and in order to secure this talent, enterprises are willing to offer a higher payscale.

As organisations experience an unexpected increase in data, their need for highly-skilled Big Data professionals and Data Engineers grow and have become the most in-demand resources. Big Data Engineers, Data Engineers and Data Scientists now form the bulwark of data science units and organisations willing to invest in top big data talent to maximise its business potential. As a result of this, we have seen a growing demand for these professionals but salaries remain steady as more and more professionals enter this field. In the data science talent stack, analysts and BI experts command the least pay package.

• Artificial Intelligence professionals are paid the highest salaries compared to their analytics peers i.e. 14.8 Lakh on median
• BI, Reporting, MIS professionals get the lowest upto 8.6 Lakh on median

Salaries Across Company Type

Essentially, five types of organizations recruit data scientists in India and it is important to look at salaries for all these company types. Firstly, captive centers are Global Inhouse Centres (GICs) for large MNCs setting up operations in India. Analytics forms one of the core areas that these captive centers work on.

Secondly, Consulting firms (Big 4 ) also provide analytics consultancy and this can be either to domestic market or to the global clients with delivery centers in India. Thirdly, we have seen a surge in domestic Indian firms like Reliance and Bajaj Electricals setting up analytics unit for enterprise wide adoption.

Fourth, large IT service providers have setup analytics competencies and provide services to global clients around analytics. Lastly, we have observed a rise of domestic analytics boutique firms providing specialised services to clients.

• Captive Analytics centers continue to be highest salary providers in analytics, with average compensation of 15.3 Lakh, almost the same as last year
• They were followed by the Big 4 Consulting firms offering a median salary of 14.3 Lakh and Domestic firms 13 Lakh analytics salaries
• Large IT firms provide the lowest analytics salaries in India at 10.8 Lakh. Their median analytics salaries have increased this year by almost 7%
• Boutique analytics firms have increased their median salaries by 13% to 11.5 Lakh
• This year we included startups in India to list. These include large new age well funded firms like Zomato, Swiggy, Paytm, Ola, Urban ladder etc.
• The median salary for analytics professionals at startups is 12.9 Lakh per annum

Salary By Education Level

As per our data, a premier institute credential is equivalent to a PhD in India. Will data scientists with PhDs outearn candidates with a Masters, certainly, but even though there is a huge demand for PhD candidates in data science, data scientists with a PhD currently form only 1% of the analytics workforce. On the other hand, a specialization from leading premier institutes (IITs) can fetch candidates a 22 lakh onwards package. This trend clearly denotes that data scientists from India’s elite engineering institutes can earn a base salary of 22 lakh while those from second-tier institutions earn a package 12 lakh. Also, the contribution of a Master’s degree is significant, given that almost 60% of analytics workforce in India has it and 7% of workforce is from premier undergrad or postgrad institution. Also, there is a demand for professionals who have some sort of formal training, reiterating the need for upskilling.

• A degree from elite engineering institutes can earn Analytics professionals almost 22 lakh onwards
• This is equivalent to doing a PhD
• Engineering graduates from other institutions and without a higher degree (Masters) command the lowest salaries in industry at 12.6 Lakh. It is apparent that upskilling through analytics related courses and broadening the skillset will help transition to higher payscale over time

Gender Gap

Similar to 2018 study, the data points to a huge discrepancy in salary by gender, with women analytics professionals earning 50% less as compared to their male counterparts. The pay gap has widened as compared to 2017-2018 research wherein the respondents surveyed reported earning 32% less vis-a-vis men in the same company. While the pay gap is even more pronounced this year, average salaries commanded by women over the last two years remains steady. On median, women data scientists command a salary of 9.2 L whereas male professionals command 13.7 Lakh.

It is a well-known fact that gender pay disparity exists in the analytics field and women participation in STEM has been very low. Given how analytics is related to coding and mathematics, women salaries are evidently less from male analytics professionals.

Over the last few years, there have been concerted efforts from the industry to increase women representation in the analytics field and close the gender pay gap, however we are yet to see any improvement in this area.

• On median, women data scientists command a salary of 9.2 Lakh whereas male professionals command 13.7 Lakh
• The average salary for women remains unchanged with women data scientists commanding a salary of 9.3 Lakh in 2017-2018

Analytics Salaries by Tools

• Python commands the highest salaries among analytics professionals – 15.1 Lakh as median.
• Python also saw the highest jump in median salaries vis-à-vis last year – an increase of 8.6%
• R, Tableau, SAS & SPSS saw a decrease in salaries compared to an year ago. SPSS saw median salaries decreasing by almost 4%.

Conclusion

India has emerged as one of the key regions when it comes to growth of Data and Analytics technology with demand for data scientists and analytics professionals booming. Governments, for instance, in Karnataka, Telangana and Andhra Pradesh are heavily investing in its start-up ecosystem, and we are seeing a rise in new-age companies — the data natives of India — Oyo, Ola, Freshworks, Byju’s, Swiggy, Zomato, Paytm entering the unicorn club. These high-growth companies are now applying data to business models and strategies in innovative way and driving the talent market with niche analytics roles.  

To cater to their demand, premier Indian universities and edtechs are now offering specialised courses and Master’s programmes in Data and Analytics, Machine Learning & AI. It will be interesting to see the talent emerging from these institutes and how it will shape the analytics market.  Another trend that will dominate the hiring market is that in order to secure top talent, enterprises are willing to pay top dollars and open doors to talent specialised skill-set. And while the heavily regulated Telecom sector continues to be the major employer of analytics, e-commerce segment also requires data scientists and are willing to pay above market rate to drive growth and innovation.

All in all, the year 2018 represented a year when digital transformation reached a peak and we saw enterprises showing an increased understanding of the value of data and how it is sharply co-related to revenue and profitability. This also underscores that senior management is investing more in their strengthening their existing analytics functions. Now, the challenge lies with professionals and key stakeholders to upskill, take a leap of faith and find new, exciting roles and work with data-driven technologies.

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The post Analytics India Salary Study 2019 – by AIM & AnalytixLabs appeared first on Analytics India Magazine.

India has witnessed a spectacular growth in the number of analytics startups over the last few years. In the last one year, we have covered numerous analytics startups that are working in the areas of healthcare, fashion, real estate, agriculture, facilitating lending decisions, emotional intelligence, voice-based solutions, hybrid TV powered by AI and many more. This is also indicative of the fact that India has emerged as a startup nation, with a robust ecosystem enabling startup founders and professionals to come up with innovative solutions in advanced analytics and kick-start their ventures.  

While many startups have risen to fame in the last one year, we bring you 10 emerging analytics startups that you could watch out for in the year 2019.

The list is in reverse alphabetical order.

1| Zendrive, Founded 2013

The team: It was founded by Jonathan Matus and Pankaj Risbood who were colleagues at Google, Facebook and Walmart Labs.

Employee strength: 100

Eureka moment: Addressing the fact that the transportation system is evolving with each passing day while working on reducing the number of road traffic deaths globally, Zendrive came up with a state-of-the-art technology platform that leverages mobile sensor data to provide actionable insights and thereby improve safety for all road users worldwide.

About the company and its analytics solutions: The startup uses data and analytics-based solutions to analyse drivers’ behaviour, predict road risks and reduce collisions. The company has analysed 160-billion miles of driving data to save lives and money for fleet management firms, insurance companies, and customers. It has built a first-of-its-kind AI-powered technology platform for fleet businesses, automobile companies, insurance firms and consumers to provide actionable insights about driving behaviour on the road, ensuring a safe and reliable drive.

Zendrive offers a driver behaviour analytics solution to customers. Their mobile-based SDK product detects and analyses driving events like hard braking, rapid acceleration, over-speeding, hard turns and risky phone use. Their algorithms then, use these events to compute a safety score for the driver.

Differentiator factor: With access to over 160 billion miles of driving data, Zendrive’s algorithms predict collision risk six times more accurately than industry leading models. This has been validated by Milliman, one of the largest global independent actuarial and consulting firms. Also, their solution is completely smartphone-based and does not involve any hardware. This saves resources on deployment and maintenance.

Growth story: Over the last few years, Zendrive has been able to add prestigious names such as  GasBuddy, HopSkipDrive, BoosterFuels, Fleetio, EverTransit, in their client list.

Funding:  Zendrive has raised a total of $20 million USD, most recently completing a Series A funding round of $13 million USD in 2016.

2| vPhrase, Founded 2015

The team: vPhrase was founded by Neerav Parekh who brings extensive experience from technology, marketing and finance into this startup.

Employee strength: 35

Eureka moment: The motivation behind vPhrase was the identification of a growing need for a tool that would help in making report generation efficient and simpler. Parekh worked towards identifying this gap and built a product to plug that gap. He was also strongly motivated to be an employment generator rather than being employment consumer.

About the company and its analytics solutions: The company’s  AI platform, Phrazor, analyses data, derive insights and then communicates those insights in words and also in multiple languages. vPhrase automates insight and generates reports using the following products:

• Phrazor is a completely customizable self-service Natural Language Generation platform with functionalities of configuring and generating customisable reports in seconds
• Explorazor is a self-service exploratory tool that helps in analysing uploaded data based on all major analysis frameworks
• Explorazor generates insights, detects outliers and anomalies and provides an in-depth analysis of the data provided
• BI Dashboard Plugin adds management commentary to your dashboards on Tableau, Power BI or Qlik. It helps to make these dashboards easier to understand and act upon.

Differentiator factor: Phrazor’s ability to marry analytics and Natural Language Generation is the key differentiator. With Phrazor, business teams don’t need to invest time in charts and tables, rather they get their actionable insights ready-made in seconds.

Growth story: The startup has been able to generate many use cases from various industries. It has worked with clients such as Kotak Bank, HDFC Securities, ICICI Lombard Insurance, Star India, Abbott Pharma, and more. vPhrase is now expanding internationally where they are now working with the likes of Fidelity International, ABB, and OCBC.

Funding: vPhrase had raised $250,000 Seed Round from Venture Catalyst, Zone Start-ups, and CIIE.

3| Thoucentric, Founded 2015

The team: The core team of Thoucentric includes Archi Bagchi, Neelakshi Kotnis, Pradeep Jadhav, Siddhartha Roy, Manish Garg, Bharat Kumar L, Satyam Tiwari, Karthik S, Ashok Babu R, Sridhar S, Prince Kumar, Elango M, Abha Bhatnagar, Rajan Jindal, Ankit Agarwal, Udit Ranjan, Prashant Bhushan Sharma, Ashish Verma, Ajay Kumar MS.

Employee strength: 170+ Globally

Eureka moment: The idea behind its inception was to create an organisation that focuses on solving complex business problems through process solutions or technology. Build by a group of strong techno-functional consultants, they are highly focused on execution.

About the company and its analytics solutions: The startup covers problems across the entire cycle of analytics including prescriptive, predictive and cognitive. Some of the examples of the value delivered by the analytics team for clients include:

• Improved short-term demand forecast by building a ‘Demand Sensing Platform’ for a Global Personal Care major
• ML Approach to Production Planning; Reduced 60% Man-Hours & improved planning forecast accuracy to 94% for a global CPG player
• ML approach to exception-based planning impacted 50% reduction in the S&OP man hours
• ML approach to ‘right’ thinking on outlet prioritization & BTL spend allocation for a global Alco-Bev player

Differentiator factor: The startup brings strong analytics experience in solving business problems. They believe in handholding the clients right from defining a problem to the last step of ensuring returns on investment are met. It is their collective passion that brings solutions hitherto unheard of and unprecedented, into fruition.

Growth story: Starting with one consultant in 2015 they have grown to a team of 170+ consultants spread across India, UK, Australia and Singapore. They began as a two-man show and are today a team comprising of data scientists, data engineers and cloud architects. Some of their clients are Unilever, Mondelez, Tata Global Beverages Limited, Asian Paints, Nivea, Marico, Trident Group, Garware Group and Diageo and others.

Funding: Thoucentric is bootstrapped and self-funded.

4| Spoonshot, Founded 2015

The team: Spoonshot was founded by Sai Sreenivas Kodur (ex Zomato) and Kishan Vasani (ex Just Eat).

Employee strength: 20+

Eureka moment: The startup was founded to put a stop to stale, rear-view insights developed by out-of-touch companies that employ archaic methodologies when it comes to FMCG sector. Founders believe that over 50 percent of new products fail due to lack of understanding and a lack of testing with consumers and most consumer insight methodologies are not evolving fast enough to capture rapidly changing trends and tastes. Spoonshot was born to address these problems.

About the company and its analytics solutions: Spoonshot (formerly known as dishq) was founded in 2015 with the aim of building a personalised food discovery app for the Indian market. dishq (a combination of dish and ishq), was focused on shifting food ordering away from ratings and reviews, to a more visual and emotionally driven decision. They changed the name to Spoonshot when they decided to pivot to a B2B audience and have an international focus.

The startup uses food science to extrapolate novel and relevant insights, personalised to every user. Their methodology is geared towards uncovering hidden product innovation opportunities by surfacing early signals for novel and emerging ingredient combinations. Their analysis gives decision-makers the quantitative confidence they need to act faster.

Differentiator factor: They leverage long-tail of open, alternative data to build proprietary structured data sets, connecting this data using machine learning, drawing signals and casualty. With these knowledge repositories, their goal is to ultimately replicate human cognition in the domain of food.

Growth story: They have seen good growth in terms of product development and building solutions that actually solve key problems in the food and beverage industry. They have plans for hiring and driving product innovation ahead. They are currently working on Spoonshot Genesis which is in private beta stage and will be launched fully in June. For Genesis’ private beta, they are currently working with 40+ large FMCG and F&B brands, 15 of which are global power-houses of food, doing billions of dollars of business every year.   

Funding: Spoonshot has raised $500,000 till date and is looking to raise another round of funding later this year.

5| Pisquare, Founded 2014

The team: Pisquare was founded by Chinmay Pradhan and Rojalin Biswal, who have more than 15 years of experience in management consultant and data analytics.

Employee strength: 35

Eureka moment: With an aim to create a smarter workforce, they are working to create a smarter set of processes with the infusion of AI into the current workflows of enterprises. PiSquare wants to take AI across the layers in a web-based system where intelligence is available on demand.

About the company and its analytics solutions: Initially started as Arima Research in 2014, they operate under the brand name of Pisquare. Pisquare partners with organisations to transform their decision-making ecosystem with the right mix of strategic planning supported by analytical insight. Using a combination of analytics and visualisation they are creating a spring board for businesses to reach the next level of performance.  They have been partnering with clients across the globe to optimise their customer engagement, supply chain, marketing & sales, delivery and talent for more aligned and mature performance. Their solutions are broadly structured into the areas of customer analytics, talent analytics and operations in IT/ITES.

Differentiator factor: They offer a combination of advanced analytics with advanced visualisation served on a web-based platform. They focus not only on the accuracy of predictions but equally on factors and inputs that are causing the effect

Growth story: Within a year of inception they have worked with companies such as Dell, Capita Plc, Mahindra Finance, Tata Capital, Sunlife, Powerschool, Department of Transport and State Government departments, major banks and insurance organisations.

Funding: They competed for a seed funding round last year

6| Pentation Analytics, Founded  2015

The team: It was co-founded by Anirban Roy (Founder Director & Chief Executive Officer), Kamal K Das (Co-Founder &Chief Operating Officer) and Pardeep K Shah (Director and mentor). 

Employee strength: 23

Eureka moment: The company targets core insurance use-cases, with an objective to deliver in the areas of soliciting & customer acquisition, risk assessment & policy development, claim assessment & customer service, fraud analytics, customer retention & cross-Sell. Using technologies such as predictive algorithms, AI, NLP, deep learning, gradient boosting trees and more, it analyses structural and unstructured data residing at various sources.

About the company and its analytics solutions: Pentation analytics, as an InsurTech and Big Data Analytics company, provides analytical services to the BFSI industry, with its core product Insurance Analytics Suite ®. The analytics products by Pentation Analytics are IAS (Insurance Analytics Suite), Pentation Insurance Scores and Quote machine. These products bring use cases such as advanced analytics across the value chain, machine learning based scores at an individual policy level, acquisition via Self-quote generation and more.

Differentiator factor: The product by Pentation Analytics is an all-in-one self-service platform, covering business intelligence & insights, planning & monitoring, policy-level scorings through ‘Ensemble scoring algorithms’, automated allocation of policy through ‘Operational optimization algorithms’, role-wise access, connection to 3rd party API and technology platforms and more.

Growth story: Pentation Analytics has been recognized at multiple international as well as national industry platforms. The Insurance Analytics Suite® by the company has already been implemented with some major insurance companies, where they have been able to increase the retention through their predictive modelling and operational optimisation. Some of their major clients are NPCI, DCB Bank, Bharti AXA etc.

Funding: They are at the early stage of funding.

7| Impact Analytics, Founded 2015

The team: The primary founder of Impact Analytics is Prashant Agrawal who has more than two decades of cross industry experience.

Employee strength: 200+ across global geographies

Eureka moment: Impact Analytics came into existence to fill a gap where clients were looking for solutions that combined best practices of management consulting along with analytic based products.  

About the company and its analytics solutions: Impact Analytics is the US-headquartered, rapidly growing analytics firm dedicated to serving clients through a synthesis of business consulting, analytics services and products. Impact Analytics offers a set of advanced analytics products and services for data-driven decision making in consumer-facing sectors such as CPG, retail, hospitality, banking, sports and gaming. Specific solutions centre around providing deep actionable insights for margin improvements, merchandising optimization, marketing analytics, customer analytics, operational excellence and AI-based automation solutions.  

Differentiator factor: Impact Analytics prides itself on being first in the market on several of the solutions which are driven by a core set of values deriving substantial savings in costs and maximising profitability for clients.

Growth story: Growth has doubled every year since its inception. Some of their major clients include at least 12 of the top 500 companies in the United States.

Funding: Impact Analytics secured $750,000 in a funding round led by early-stage VC firm Aarin Capital which is a tie-up between TV Mohandas Pai and Dr Ranjan Pai. Other investors who participated in this round include Michael Herzig, serial entrepreneur and Ashish Lakhanpal (CEO Kismet Capital).

8| Dataweave, Founded 2011

The team: The company was founded by Karthik Bettadapura and Vikranth Ramanolla.

Employee strength: 140+ employees

Eureka moment: During Bettadupura’s previous role as the lead programmer of the data team at Web18, he recognised the market potential of providing a solution that harnesses publicly available data and analyses the external factors that impact businesses. This thought process led to the formation of DataWeave in 2011 along with Ramanolla, who shared Karthik’s vision and passion.

About the company and its analytics solutions: DataWeave provides competitive intelligence to e-commerce businesses and digital shelf analytics to consumer brands by aggregating and analysing data from the web at a massive scale. The company’s AI-powered technology platform enables e-commerce businesses to make smarter pricing and merchandising decisions to drive profitable growth, and consumer brands to govern their online brand presence, optimise their Share of Voice, and improve their e-commerce shelf velocity.

DataWeave offers SaaS-based product suites called Retail Intelligence and Brand Analytics that helps companies protect their brand equity online and optimise the experience delivered to shoppers on e-commerce websites. Popular use cases include monitoring and resolving minimum advertised price (MAP) violations, detecting counterfeit product listings, tracking and improving the share of voice of online promotions, optimising their digital shelf velocity, and more.

DataWeave also provides investment firms and hedge funds with Retail Alternative Data, which enable them to take data-driven buy or sell positions in the market. 

Differentiator factor: DataWeave stands apart from its competitors in several ways. Their human-aided machine intelligence based technology platform leverages proprietary NLP and Computer Vision technologies to enable accurate and rapid data processing and insights generation. Their technology platform is also language agnostic, currently supporting over 25 international languages. They support diverse delivery mode options including API, CSV, PPT, FTP for easy and speedy consumption.

Growth story: DataWeave is one of very few SaaS startups of Indian origin that has replicated its domestic success internationally, particularly in the US. They have a strong presence in India, the Middle East, and SouthEast Asia and the US.

Funding: Following a seed funding round in 2011, they announced Series-A round of financing in April 2017, which was led by FreakOut Group, Herb Madan – a Silicon Valley investor, and a diverse group of institutional investors from US, India, and Singapore. Other investors include Blume Ventures, M&S Partners, Rajan Anandan, Times Internet, and WaterBridge Ventures.

9| Crediwatch, Founded 2016

The team: It was founded by Meghna Suryakumar and Sandeep Anandampilla.

Employee strength: 36 employees across two locations

Eureka moment: The founders state that India has over 50 million unregistered businesses and over 1.13 million active registered companies but only 7000 of these are listed offering detailed while the rest exist in the ‘Dark Space’. There is an estimated $1000bn annual business entered with these ‘dark space‘ companies as a combination of credit, trading and other agency related activities. Crediwatch was created as a cost-effective and scalable solution to provide transparency and information about these businesses.

About the company and its analytics solutions: Crediwatch is an insights-as-a-service platform that deploys scalable deep learning tools across disparate digital footprints left by private entities (big and small) to provide dynamic credit management as a service to financial institutions. It uses advanced computational techniques such as AI, ML and NLP to derive near-real-time insights from structured and unstructured datasets.

Crediwatch has created an Early Warning System (EWS) that banks and NBFCs can use to monitor their loan portfolios in near real-time using a combination of public and private data. This system uses globally-used models trained on Indian corporate datasets and predicts delinquencies and defaults 12 to 18 months before they occur.

Differentiator factor: Their proprietary predictive algorithm has been developed and tested on over 9000 Indian companies. Crediwatch provides singularity of insights by tapping into 2500 data sources and tying the results into a singular truth hence allowing for a verified and accurate delivery of insights. Other differentiating factors are zero human touches, platform agnostic solutions, scalability and more.

Growth story: Since 2016, Crediwatch has provided credit analytics for a portfolio of over INR 50,000 crores across 20+ BFSI clients analysing over 1.2 million data tokens along the way. Some of Crediwatch’s clients are  Barclays Bank, Capital Float, RBL Bank, SBI, Trilegal etc.

10| BDB, Founded 2015

The team: Avin Jain (Founder, CEO), Anoop VP (CTO), Vishal Venugopal (VP AI) and several others founded BDB with a focus on end-to-end data, analytics, AI platform.

Employee strength: 120+

Eureka moment: Being in BI consulting for the initial four years the founding team realised the pain points of the analytics industry and that many existing analytics tools were unable to fulfil the analytics demands of customers. There was no single platform which could address end to end analytics requirement and therefor BDB platform was founded as a single end-to-end analytics platform to address these challenges.

About the company and its analytics solutions: The BDB Decision Platform brings analytics within the reach of every individual by enabling seamless workflows for all user groups including CXO, business user, citizen data scientists and more. With customers in 10 different verticals, BDB platform has been deployed in education, retail, healthcare, life science, BFSU and other domains. BDB has a cloud-based SaaS Analytics Platform for SMEs – Yujaa which internally uses a set of business intelligence tools that gives a 360° view of data. Yujaa provides role-based solutions to various industries that are affordable and accessible by any user across the globe.

Differentiator factor: BDB platform provides a comprehensive experience of data analytics offering plugins such as data pipeline and data wrangling all tacked together in a simple drag and drop manner. BDB can be deployed on-premise or accessed as a cloud-based application. The product is built on microservices architecture and contains all the features of a modern, highly scalable, secure, multi-tenant analytics platform.

Growth story: BDB has been growing significantly with its solutions available on cloud and on-premise. It has earned more than $8 million through its platform in the last four years and has worked with a few fortune 500 companies in different verticals such as enterprise customers, SMEs and more.

Funding: Currently BDB is looking for Series A funding.

The post 10 Emerging Analytics Startups In India To Watch Out In 2019 appeared first on Analytics India Magazine.

As one of the premier institutes for technology, Massachusetts Institute of Technology (MIT) has several prominent research which has resulted in many ground-breaking technological advancements.

In this article, we take a look at the top five recent research papers from on Computational Linguistics from the institute.

1) Learning an Executable Neural Semantic Parser

Authors: Jianpeng Cheng, Siva Reddy, Vijay Saraswat, and Mirella Lapata

Abstract: This article describes a neural semantic parser that maps natural language utterances ontological forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generated tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language.

Research methodology: To tackle mismatches between natural language and logical form tokens, various attention mechanisms were explored. Finally, the researchers considered different training settings for the neural semantic parser, including fully supervised training where annotated logical forms were given, weakly supervised training where denotations were provided, and distant supervision where only unlabeled sentences and a knowledge base are available.  

2) Unsupervised Compositionality Prediction of Nominal Compounds

Authors: Silvio Cordeiro, Aline Villavicencio, Marco Idiart and Carlos Ramisch

Abstract: Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments.

Research methodology: For evaluation, the researchers introduced data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they were able to incorporate information about idiomaticity.

3) Automatic Inference of Sound Correspondence Patterns across Multiple Languages

Authors: Johann-Mattis List

Abstract: The researcher presented an automatic method for the inference of sound correspondence patterns across multiple languages based on a network approach. The core idea was to represent all columns in aligned cognate sets as nodes in a network with edges representing the degree of compatibility between the nodes.

Research methodology: The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique. The resulting partitions represent all correspondence patterns that can be inferred for a given data set. By excluding those patterns that occur in only a few cognate sets, the core of regularly recurring sound correspondences can be inferred. Based on this idea, the article presents a method for automatic correspondence pattern recognition, which is implemented as part of a Python library which supplements the article.

4) A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots

Authors: Yu Wu, Wei Wu, Chen Xing, Can Xu, Zhoujun Li, and Ming Zhou

Abstract: The researchers studied the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task involved matching a response candidate with a conversation context, the challenges for which include how to recognize important parts of the context, and how to model the relationships among utterances in the context.

Research Methodology: Using a new matching framework called sequential matching framework (SMF), the researchers proposed a sequential convolutional network and sequential attention network and conducted experiments on two public data sets to test their performance. Experiment results show that both models can significantly outperform state-of-the-art matching methods. The researchers also show that the models are interpretable with visualisations that provide us insights on how they capture and leverage important information in contexts for matching.

5)Parsing Chinese Sentences with Grammatical Relations

Authors: Weiwei Sun, Yufei Chen, Xiaojun Wan and Meichun Liu

Abstract:  The research represents grammatical information using general directed dependency graphs. Both only-local and rich long-distance dependencies are explicitly represented.

Research methodology: To create high-quality annotations, the researchers took advantage of an existing TreeBank, namely, Chinese TreeBank (CTB), which is grounded on the Government and Binding theory. Two key problems as addressed by the researchers include (a) how to decompose a complex graph into simple subgraphs, and (b) how to combine subgraphs into a coherent complex graph. For transition-based parsing, the researchers introduced a neural parser based on a list-based transition system. They also discussed several other key problems, including dynamic oracle and beam search for neural transition-based parsing. The evaluation gauged how successful GR parsing for Chinese can be by applying data-driven models. The empirical analysis suggests several directions for future study.

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