The analytics industry has been continuously advancing and India is becoming an emerging hub for analytics solutions across the globe. As the demand keeps growing exponentially, the sector is bound to witness rapid growth.

Within analytics, risk analytics is one segment which has lately been under the spotlight because of its increasing demand. Studies estimate that the global risk analytics market is likely to more than double itself to reach USD 50 Billion by 2020. This significant growth in the risk analytics market can be attributed to the rising need of organizations to minimize their losses which are incurred due to risks, maximize their ROI, and enhance the decision making process.

With growth in risk analytics, the requirement for talented risk analyst professionals is also on the rise. And because of the already existing shortage of talent pool in analytics industry, the demand for Risk Analytics Professionals is extremely high converting into huge salary packages for the analytics professionals.

About Great Lakes

Founded in 2004, Great Lakes Institute of Management is one of the top B-Schools in India. Led by exceptional academic faculty, steered by an outstanding advisory council and buoyed by the international collaborations, Great Lakes has, within a short span of 10 years emerged as a top-ranked business school.

Great Lakes’ Post Graduate Program in Business Analytics, started in 2013, is India’s No 1 Business Analytics Program. Since its launch, the program has trained 1000+ working professionals with almost 66% of Great Lakes PGP-BABI alumni transitioning into analytics roles and profiles within 6 – 12 months from graduating from the program.

Introducing CFRA & its features

The Great Lakes Certificate Program in Financial Risk Analytics is an industry collaboration specialized program designed for professionals who want to build their careers in the financial risk analytics industry.

The program begins with a primer in statistics and is followed by laying a strong foundation in quantitative methods, financial instruments and markets. With the requisite statistical and financial foundation in place, the candidates then get trained on exhaustive modules, techniques and case studies in Market Risk and Credit Risk.

One of the defining features of the program is the coverage of Basel implementation comprising a thorough understanding of Basel Norms followed by its implementation aspects such as IRB, Risk capital calculations, CCAR, Capital adequacy, calculation of risk weights etc.

The program is internationally recognized and is a dual certificate program. The participants will get certification from Illinois Institute of Technology, Chicago (USA) in addition to the certificate from Great Lakes Institute of Management.

Dr. Bappaditya Mukhopadyay, Program Director (CFRA) on introduction on CFRA “Great Lakes has been a pioneer in high quality analytics education in India with our executive program in Business Analytics being consistently ranked as the best in the country. Having delivered over 250,000+ hours of analytics learning content to working professionals in India and abroad, we have collaborated with the industry to develop a dedicated analytics program in Financial and Risk analytics – CFRA.”

Why the need for this course/specialization?

Risk Analytics has been an area of interest to companies and organizations are looking to create a talent pool which can help them gain deeper insights into this field. There is tremendous demand for risk analysts due to the gap in the demand and supply of analytic professionals worldwide. And India is arising as a hot destination globally for analytics talent due to the skill sets possessed by professionals in India.

Great Lakes being a premier institute in India for analytics is constantly approached for providing analytics talent pool and with rising need in the risk analytics area; companies are looking for talent pool with specialization in Risk Analytics. These requirements from the industry propelled Great Lakes to collaborate with industry to offer a program in Risk Analytics.

CFRA has been designed as a specialized program catering to the Analytics industry need of more- trained talent in financial and risk analytics. If you take a quick glance of all the analytics profile the industry is seeking, finance and risk analytics tops the list by far. The industry needs analytics professionals who have the analytical ability to identify, measure and mitigate financial risks within the overall regulatory framework. This would mean a program that blends the market risk, the credit risk as well as the operational risk within the overall Basel guidelines.

Dr. Bappaditya Mukhopadyay commented “Through this program, the participants must be able to value simple as well as complex financial products, understand and develop risk models as well as inculcate the abilities to implement Basel requirements and CCAR.”

Details of the CFRA Program

Who is it for?

Great Lakes CFRA program is ideal for candidates who wish to excel in the domain of risk analytics. Candidates should have a minimum of 2 years of experience while applying to the program. While a prior work experience in banking, finance or consulting is an advantage, candidates from other industries (such as IT/ITES) serving financial clients are also a good fit.

What to expect?

On completion of the program, a candidate must be able to appreciate and solve the following problems:

• How does a financial institution identify, measure and manage market risk
• How are credit risk models developed and validated
• How are stress testing of such models undertaken
• How are regulatory capital (CCAR) calculated
• How to setup systems that comply with Basel guidelines

The curriculum entails exhaustive coverage of academic concepts blended with rich industry exposure and hands-on training to ensure that the candidates graduating from CFRA program are industry ready.

Program Format

The program duration is 6 months and is a combination of weekend classroom sessions, online lectures and recorded videos / pre-reads. The program covers 160 hours of learning comprising 100 hours of classroom learning and 60 hours of online learning. The calendar is designed such that most classes are conducted on weekends and public holidays, thereby causing minimal disruption to the work schedule.

Fee for the program: The fee for the program is Rs.3, 00,000 exclusive of Service Tax

Batch Commencement Dates: December, 2016

Centre: Gurgaon

For more details about the program visit https://goo.gl/Y2728u

The post Great Lakes launches program with Financial Risk Analytics as a Specialization appeared first on Analytics India Magazine.

Artificial Intelligence is currently the most trending technology term in the industry. Though Artificial Intelligence is not a new concept, what comes as a baffling surprise is that analytics is being re-christened as artificial intelligence today. A lot of it to do with companies / consultants trying to on-board the frenzy around artificial intelligence. But, analytics and artificial intelligence are completely different technologies with some similarities in implementation and almost little overlap in terms of end results.

Analytics as an industry is already heavily jargonized and it important to clear the air building up around analytics being called artificial intelligence at lot of instances.

First, the overlaps

Analytics is an encompassing field that uses mathematics, statistics, computing and machine-learning techniques to discover insights hidden deep in the recorded data. Analytics helps enterprises understand their current scenario and discover the future steps to achieve growth as analysis of data fuels knowledge discovery.

Artificial intelligence is about creating systems (call them machines) that can mimic human intelligence as closely as possible. How close? – Well, lets say a Turing Test can tell that. Artificial Intelligence is not a new phenomenon; it’s almost as old as computing itself. We already have seen similar frenzy around AI like today’s, atleast twice in the past followed by deep AI winters where funding dried up for all research and initiatives around AI.

But this time around, it’s looking different for AI. Partly because there are some concrete results that have surfaced. Though the real AI where computer systems can reach the level of human intelligence and behavior is still out of reach, the reason AI today has better results is because it is using the same concepts that analytics is using.

Earlier efforts in AI were centered around creating expert systems, which at the most basic levels were nothing more than rule-based algorithms. What’s happening today in AI is almost the same, but with much higher sophistication. Today, AI systems are using power of data, computing and statistics to create intelligence systems. Sounds like that’s exactly what analytics is. But, the similarities end here.

Now, the differences

When we hear a computer winning a chess match against the world’s best chess champion, we say the computer is intelligent. When our smartphone understands our voice commands, we call it intelligent. These are all forms of artificial intelligence and are build on top of a combination of data, computing and machine learning.

On the other hand, recommendation engines, forecasting loan defaults or predicting fraudulent transactions or spam emails are applications of analytics and are also build on top of data, computing and machine learning. But these applications cannot be termed artificial intelligence.

Organizations only need to know the right question and analytics will give them the outcome they need to focus on. Analytics helps to produce fast insights required to make fact-based decisions and allows us to find out answers to questions we might never have thought to ask.

Artificial Intelligence can be explained as the study of man-made computational devices and systems which can be made to act in an intelligent manner.

The very core of both AI and analytics can be same in terms of how they are being implemented. Yet, what they are used for, have completely different objectives. It would only be fair on our part to keep these terms to what they really mean. This would also bode well for the whole industry in long term.

The post Don’t mix Analytics with Artificial Intelligence appeared first on Analytics India Magazine.

After 2 long months of in-depth and rigorous study, Analytics India Magazine is out with its annual ranking for ‘Top 10 Analytics Courses in India’. This being the fourth in the row, AIM has always been positively supporting its aspiring data scientists by providing them insights into the world of analytics education. We received 16 nominations for this study of which we have selected the finest, the best, top 10 analytics courses for all our aspiring analytics professionals.

The institutes have been ranked on 6 parameters i.e. Course Content, Pedagogy, External Collaborations, Faculty, Brand Value, and Other Attributes like, Placement Assistance, Virtual Labs Events, LMS, etc. Each participating institute has been rated on the scale of 1-5 (where 1 is for Worst and 5 is for Best) for all 6 parameters individually to arrive at an overall ranking. Equal weights were assigned to all parameters for the ranking process. Apart from these parameters, the study also considers student as well expert feedback before arriving at ‘Top 10 Analytics Courses in India’ for the year 2016.

Please note, this ranking is for long term analytics programs offered by Universities / B-schools in India. This does not include training institutes for analytics.

1.  PGP in Business Analytics & Business Intelligence – Great Lakes Institute of Management

• Headquarter City: Chennai
• Cities of Operation: Chennai, Bangalore, Gurgaon, Pune, Hyderabad
• Year of inception: 2004
• Duration of Program: 12 months
• Mode of Delivery: Weekend Classroom + Online

Great Lakes, one of the leading Business Schools in India, envisions of providing the corporate world with Business-ready Leaders. And it was in line with this vision, Great Lakes introduced their Post Graduate Program in Business Analytics (PGP-BA) to bridge the existing talent gap in the analytics industry. They have been among the first few institutes who came up with a program in Analytics.

Course Content (Rating 4.9): Great lakes analytics program blends Academic Excellence with Business Relevance to equip its students with skill sets required for managerial, techno-functional roles in Analytics. The curriculum is Industry Relevant which builds on the analytical foundation and industry oriented applications.

Pedagogy (Rating 4.8):  Great Lakes adopts a Blended Learning Environment (Weekend Classroom + Online) pedagogy to causes minimal disruption to work schedule.  The program is designed to transform candidates to business ready analytics professionals through hands on experiential learning.

Faculty (Rating 4.8): PGP-BABI being an industry application focused course has a large number of industry experts teaching. Around 50% of the classroom learning hours are delivered by distinguished industry experts.

External Collaborations (Rating 4.7): Great Lakes PGP-BABI is internationally recognized by Illinois Institute of Technology, Chicago, USA. Successful participants of the PGP-BABI program get a dual certification – a certificate from IIT, Chicago, USA in addition to the certificate from Great Lakes Institute of Management.

Brand Value (Rating 4.7): Founded in 2004, Great Lakes has, within a short span of 12 years, emerged as a top-ranked Business School. Great Lakes has lot of accreditations to its name. In 2014, Great Lakes was accredited by Association of MBAs (AMBA, UK) for its PGPM and PGXPM programs and became the youngest B-School in India to receive this prestigious international accreditation.

Other Attributes (Rating 4.8): The end-to-end career support activities are provided at Great Lakes PGPBA. Almost 66% of the PGP-BABI alumni have transitioned to Analytics roles either within their own company or in a new company.

The overall rating for Great Lakes Institute of Management is 4.78

2.  Business Analytics and Intelligence – Indian Institute of Management (IIM B)

• Headquarter City: Bangalore
• Cities of Operation: Bangalore
• Year of inception: 1973
• Duration of Program: 1 year
• Mode of Delivery: Classroom; Classroom and Distance mode (online)

Established in 1973, IIM B focuses on partnering with industry and leading academic institutions, the world over to enhance the output from their courses including their analytics program Business Analytics and Intelligence

Course Content (Rating 4.8): The course is designed to provide in-depth subject knowledge on basic and advanced statistics in addition to learning tools and techniques. Participants are encouraged to solve case studies to understand concepts better along with a capstone assignment post each module. A real life Industry project for a minimum of 6 months is mandatory as a part of the course to provide students with industry experience

Pedagogy (Rating 4.8): IIM B implies multiple ways to make the course more industry specific and practical in nature. Their principal way of teaching is Case-based teaching and lot of practical exercises during the sessions to give a hands-on exposure to participants.

Faculty (Rating 4.9): All the faculties at the institute have a PhD showcasing the experience of their faculties. Also numerous Speakers from analytics Industry participate as guest faculty.

External Collaborations (Rating 4.6): IIM has external collaborations with SAS, R and Python training consultants, Qlik team for imparting technical knowledge. Also the institute has industry connect with some of the prestigious organization for promoting good Industry-Student connect.

Brand Value (Rating 4.9): IIMB is the only Indian business school to feature among the Top 50 B-schools on the Financial Times Executive Education 2015 Rankings and has recently topped the list of best management institutes in the ‘India Ranking 2016’–the first-ever national ranking of universities by the Government.

Other Attributes (Rating 4.5): The institute organizes various events like conferences, workshops, and special classes in the field of analytics. The institute has Data center and analytics lab which gives access to census data and other data sources, and Big data lab for participants to run through larger datasets.

The overall rating for IIM B is 4.75

3.  Postgraduate Diploma in Business Analytics (PGDBA) – IIM Calcutta, ISI Kolkata, IIT Kharagpur (Tri-institute program)

• Headquarter City: Kolkata and Kharagpur
• Cities of Operation: Kolkata and Kharagpur
• Year of inception: 2015
• Duration of Program: 2 years
• Mode of Delivery: Classroom

3 institutes – IIM Calcutta, ISI Kolkata, IIT Kharagpur have come together to offer this unique Tri-institute program in analytics. The program is a two year full time residential program and is designed to create Business analysts and Data Scientists with skills in Statistics, Computer Science and Management.

Course Content (Rating 4.9): The highlight of the course is that it is a tri-institute program. Each of the three institutes focus on a separate area of business analytics, in accordance with its expertise and competence. Also there is hands-on business analytics training at a related company and  continuous interaction with industry leaders throughout the course to make students industry ready.

Pedagogy (Rating 4.9): The curriculum across the three institutes is taught with a balanced mixture of theory and praxis. The program leverages the strengths and mutual complementarities of the three institutes.

Faculty (Rating 4.8): The students are taught by reputed faculty as well as industry leaders from each of the three institutes. Currently 51 faculties are teaching a batch of 60 students.

External Collaborations (Rating 4.5): The program involves interactions with industry leaders from organizations such as Microsoft, Xerox, SAS, American Express, SBI, Deloitte, KPMG, PwC, Flipkart.

Brand Value (Rating 4.9):  All the three participating institutes are globally renowned institutes. IIT K has bagged the National IP award 2016 for Top Indian Academic institution for Patents. IIM C was the first management institute in India to be credited by AACSB, AMBA and EQUIS.

Other Attributes (Rating 4.2): Placement assistance is provided by a separate committee involving all the three institutes. The placements are conducted at IIM Calcutta campus

The overall rating for this tri-institute program is 4.70

4.  Certificate program in Predictive Business Analytics – BRIDGE School of Management

• Headquarter City: Gurgaon
• Cities of Operation: Gurgaon, Noida, Bangalore
• Year of inception: 2013
• Duration of Program: The executive program duration is 51 weeks. The fresher program duration is 49 weeks
• Mode of Delivery: Classroom & Online mode

Bridge School of Management is a flagship Business School launched via a joint venture between HT Media Ltd. & Apollo Global, Inc. (USA). Bridge School offers programs in various field including Analytics (Certificate program in Predictive Business Analytics).

Course Content (Rating 4.7):  The course is jointly offered with Northwestern University, one of the universities with leading Analytics program. Curriculum has been designed jointly by faculties of both institutes using inputs from industry practitioners in the Analytics domain to make student industry-ready.

Pedagogy (Rating 4.6): The program is delivered in a blended mode – classroom and Northwestern University Virtual learning Environment – Canvas.  Analytics experts from leading organizations bring in industry examples and perspective to the program adding value to the student’s learning curve.

Faculty (Rating 4.4): 50% of the Analytics faculty from Northwestern and Bridge School are either PhD or Research Scholars (pursuing PhD). Also analytic industry experts participate in delivering core lectures to the analytic students.

External Collaborations (Rating 4.4): Bridge School has an exclusive academic collaboration with Northwestern University School of Professional Studies. Also Bridge School is a Registered Educational Provider for Project Management Institute.

Brand Value (Rating 4.6): Backed by India’s media leader HT Media and Global Education group-Apollo Global (USA), BRIDGE School leverages best-in-class knowledge, expertise and technology for an innovative learning environment and industry relevant programs.

Other Attributes (Rating 4.5): Bridge’s career management services team networks with the industry, academia and the students to lead the entire placement process. The institute hosts Hackathons to train their analytics professionals

The overall rating for Bridge School of Management is 4.53

5.  Executive Program in Business Analytics – MISB Bocconi and Jigsaw Academy

• Headquarter City: Mumbai
• Cities of Operation: Powai, Mumbai
• Year of inception: 2014
• Duration of Program: 10 months
• Mode of Delivery: Classroom and Online

Jigsaw Academy was founded in 2011 by Gaurav Vohra and Sarita Digumarti to provide quality training in the field of analytics and Big Data. Executive Program in Business Analytics is their flagship course.

Course Content (Rating 4.4): The course is a healthy mix of Analytics and Big data. It focuses on live proprietary case studies in Big Data and Analytics to give hand-on experience to students.

Pedagogy (Rating 4.5): The pedagogy adopted is to strike a balance between work and study with a blend of online and offline classes.

Faculty (Rating 4.6): This certification program includes distinguished faculty from SDA Bocconi, Milan and industry experts apart from in-house faculties

External Collaborations (Rating 4.6): Jigsaw has partnered with IIMB for Big Data analytics course training and content development. Also it offers content for all analytics electives taught at SOIL in addition to teaching for the same.

Brand Value (Rating 4.4): Jigsaw has become a preferred choice of students, universities and companies for their data science training requirements. And its alumni base of over 40,000, students worldwide speaks about its brand value in the education industry.

Other Attributes (Rating 4.4): Jigsaw provides complete placement assistance and also has virtual labs and LMS facility for its students.

The overall rating for Jigsaw Academy is 4.48

6.  Post Graduate Program in Business Analytics – Praxis Business School

• Headquarter City: Kolkata
• Cities of Operation: Kolkata and Bangalore
• Year of inception: 2007
• Duration of Program: 9 months
• Mode of Delivery: Classroom

Praxis Business School was early to recognize the need for trained analytics resources and introduced the first one-year full-time analytics program in the country in 2011, their flagship course being PGP in Business Analytics.

Course Content (Rating 4.6): The Course aims at equipping students with the tools, techniques and skills to enable a seamless absorption into the domain of Analytics and grow into the roles of Data Scientists. The program is co-created and co-delivered with Knowledge Support from PwC and ICICI Bank.

Pedagogy (Rating 4.5): Praxis adopts a pedagogy which his practical in nature. Students during the course are exposed to a set of near real world projects. Faculty use data available from Kaggle competitions to create assignments for students and their solutions are benchmarked against global leaderboards.

Faculty (Rating 4.5): 65% of the faculty members teaching analytics subjects are associated with Analytics organizations . Also guest faculty from Praxis knowledge partners, namely ICICI Bank and PwC are part of the teaching process.

External Collaborations (Rating 4.3): Praxis has quite a few external collaborations 9PWC, ICICI Bank, Abzooba Inc, Ericsson Global Services, IBM Watson Labs, Modelytics) with industry professional to deliver certain special analytics classes during the course.

Brand Value (Rating 4.2): Praxis Business School has been the pioneer in bringing in full time education program in the field of analytics. And the program has received overwhelming response from the students and the industry. It initiation has now been followed by several institutes launching program in analytics.

Other Attributes (Rating 4.5): Praxis has a formal placement process to generate quality opportunities for internships followed by final placements.

The overall rating for Praxis Business School is 4.43

7.  Post Graduate Program in Data Science, Business Analytics and Big Data  (PGP-DS-BA-BigData) – Aegis School of Business, Data Science & Telecommunication

• Headquarter City: Mumbai
• Cities of Operation: Mumbai, Pune
• Year of inception: 2002
• Duration of Program: 11 months, 9 months of training (3 terms each of 3 months) + 2 months of Internships
• Mode of Delivery: Classroom, Online, Hybrid Model

Aegis School Of Business, Data Science & Telecommunication offers various programs in 25 countries to top executives from top IT/Telecom firms. In 2015 Aegis joined hands with IBM to offer high end courses in the field of Data Science, Business Analytics, Big Data, Cloud Computing & Mobility. Their flagship program being PGP-DS-BA-BigData

Course Content (Rating 4.2): Aegis’s analytics program in association with IBM is designed with the help of leading Data Scientists to meet the Data Scientist’s skills and competencies framework. A wide range of core and elective courses are offered to provide freedom to participants to design the program suiting to their and industry needs.

Pedagogy (Rating 4.5): Aegis focuses on making its students industry-ready and this is reflective in their pedagogy. The institute brings together the current software content, real-world industry experiences and hands on exposure to give the participants a practical exposure in the field of analytics.

Faculty (Rating 4.3): This program is delivered by Data Scientist engaged in real-life Data Science and Big Data Analytics projects from around the world with 45% of faculties having a PhD.

External Collaborations (Rating 4.5): Aegis has collaborated with IBM to offer high end courses in the field of Data Science, Business Analytics, Big Data, Cloud Computing and Mobility. MTNL, a leading Govt. of Indian telecom service provider, is Aegis’ Infrastructure partner in Mumbai.

Brand Value (Rating 4.3): The focus of Aegis is on technology and analytics is a perfect extension to it. Aegis offers various programs in 25 countries to top executives speaking high of its brand value.

Other Attributes (Rating 4.7): Career Management Center (CMC) at Aegis facilitates all students’ paid internship and final placements. Also Aegis and IBM have set up an IBM Business Analytics and IBM Cloud Computing Lab to help students and faculty members enhance their analytic skills.

The overall rating for Aegis School of Business, Data Science & Telecommunication is 4.42

8.  Certificate Program in Business Analytics – Narsee Monjee Institute of Management Studies (NMIMS), Bangalore

• Headquarter City: Mumbai
• Cities of Operation: Bangalore
• Year of inception: 2009
• Duration of Program: 12 months
• Mode of Delivery: Classroom

Incepted in 2009, NMIMS Bangalore, is active in the area of Executive education and runs General Management and Analytics program (Certificate Program in Business Analytics) focused on working professionals.

Course Content (Rating 4.2): The course content aims at preparing working executives for a career in Analytics by training them on basic, advanced and application focused courses. The curriculum design and program structure is prepared in consultation with Analytic Board of Governors,a body consisting of eminent professionals from industry.

Pedagogy (Rating 4.4): The courses are delivered through lectures, case discussion, lab sessions, assignments, group tasks and projects. The participants are evaluated through home assignments, projects and exams.

Faculty (Rating 4.5): Faculty for the program consists of permanent and visiting faculty members with 60% having PhD. Visiting faculty members are Analytics professionals from various organizations.

External Collaborations (Rating 4.5): NMIMS Bangalore has collaboration with SAS Institute for providing SAS tools and conducting Workshops in various areas of Analytics.

Brand Value (Rating 4.6): Eduniversal has ranked NMIMS Bengaluru as 14th in General Management in Central Asia, which is a testimony to the continuous progress that NMIMS Bangalore has made in a short span of time. NHRD also ranked NMIMS Bengaluru amongst the top 5 emerging business schools for 2015.

Other Attributes (Rating 3.9): The institute has a full-fledged placement department consisting of a placement director and 5 staff members to assist students.

The overall rating for NMIMS Bangalore is 4.35

9. PG Diploma Program in Data Analytics –  IIIT Bangalore & UpGrad

• Headquarter City: Mumbai (UpGrad), Bangalore (IIIT-B)
• Cities of Operation: Online hence Global (UpGrad), Bangalore (IIIT-B)
• Year of inception: 2015 (UpGrad), 1999 (IIIT-B)
• Duration of Program: 11 months
• Mode of Delivery: Online

UpGrad, founded by media stalwart Ronnie Screwvala, is an online higher education platform providing industry relevant programs. The International Institute of Information Technology, Bangalore focuses on education and research in IT.

Course Content (Rating 4.0): UpGrad and IIIT-Bangalore have collaborated to develop this online 11-month Program in Data Analytics. The program offers the right blend of statistics, technical and business knowledge to ensure that participants learn exactly what the employers need.

Pedagogy (Rating 4.1): The teaching style adopted is case-led, wherein faculty and industry experts use a series of industry relevant examples to teach complex analytics concepts.

Faculty (Rating 4.2): IIIT-Bangalore and UpGrad faculty are majorly from the analytics domain with 67% having a Phd. Also industry experts are involved in designing and executing the program as they bring in industry perspective.

External Collaborations (Rating 4.0): The program has established flagship partnerships with Analytics Leaders like Uber, Genpact, and Gramener. Further, the program has been built in collaboration with 30+ Analytics industry experts from leading corporations.

Brand Value (Rating 4.2): UpGrad’s brand value is reflective in its partnership with 50+ companies like Star TV, Disney, Google, Microsoft, to develop program content and provide mentorship to students. IIIT-B, with its model of education, and industry interaction, has grown in stature over time to become an institution of considerable repute in academic as well as corporate circles.

Other Attributes (Rating 4.0): UpGrad and IIIT-Bangalore will be providing placement assistance. A Virtual Cluster for the Big Data course is set up for participants.

The overall rating for UpGrad is 4.08

10. Business Intelligence and Big Data – IMT  Ghaziabad

• Headquarter City: Ghaziabad
• Cities of Operation: Ghaziabad
• Year of inception: 1980
• Duration of Program: 11 weeks
• Mode of Delivery: Classroom

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

Course Content (Rating 3.8): The course gives you a blend of industry knowledge, concepts and experiential learning through collaborative teaching by industry experts.

Pedagogy (Rating 3.9): The pedagogy will be a mix of lectures, experience sharing, real life case discussion, assignments and industry/research based projects.  The course is focused on strategic issues with cases as the primary vehicle for learning.

Faculty (Rating 3.7): Faculties include a mix of in-house and industry exports with 98% of in-house faculties having PhD.

External Collaborations (Rating 4.0):  IMT has many industry associations and international collaborations. IMT has 50+ International collaborations towards various academic modules co-teaching and joint learning.

Brand Value (Rating 4.3): IMT Ghaziabad is an institute that has been there for long and is recognized for the courses its offers and for the quality of students that come out of the institute to build their names in industry.

Other Attributes (Rating 3.9): Students opting for this course are provided with placement support. ICDM, a data management conference is organized by the institute.

The overall rating for IMT Ghaziabad is 3.93

The post Top 10 Analytics Courses in India – Ranking 2016 appeared first on Analytics India Magazine.

And here we are with yet another independent study on the job scenario of analytics and the related fields. The Jobs study 2016 is a cumulative effort of the research done by Analytics India Magazine and Jigsaw Academy.

Be it the big data expertise, data scientist or any other data analytics role, with the innumerable companies starting up in the analytics space at a fast pace, there has been a definite surge in the job opportunities.

This study brings to you recruitment trends, educational qualifications, experience and various other interesting facts on the table.

Read Analytics India Jobs Study 2013

Read Analytics India Jobs Study 2012

Top Trends in Analytics Jobs

• The number of analytics jobs rose by 29% from June 2014 to June 2015 compared to 120% increase from June 2015 to June 2016.
• Overall, there has been a 53% increase in the number of new job postings in analytics, this year compared to a year earlier.

Analytics Jobs by Cities

• In terms of cities, Bengaluru accounts for around 27% of analytics jobs, standing out to be analytics capital, followed by Delhi/NCR at 23% and 15% from Mumbai. These 3 cities together account for almost 65% of total analytic jobs.
• Hyderabad and Chennai contribute 9% each.

Analytics Jobs by Industry

• Analytics jobs are concentrated in only few sectors. This is suggestive by the fact that 81% of analytics job opportunities exist in 4 sectors viz. Banking and Financial Services, Ecommerce, Pharma/Healthcare, Energy & Utilities.
• A big chunk, almost 42 percent of analytics job opportunity comes from Banking and Financial Services industry followed by the ecommerce sector and Pharma/Healthcare at 14% and 13% respectively
• The contribution of Retail/ CPG to new job openings this year is just 5% of all analytics jobs.  

Education requirement by Analytics jobs

• Almost 44% of analytics job openings are looking for a B.E./ B.tech. degree in the incumbent while 26% analytics job openings are looking for a postgraduate degree.
• So, overall, 80% of all employers are looking to hire analytics professionals with either an engineering degree or a postgraduate degree.

Experience requirement by Analytics Jobs

• Around 62% of analytics requirements are looking for candidates with less than 5 years experience whereas 18% analytics jobs are looking for freshers.
• 31% analytics job openings are for professionals with 5-10 years’ experience to serve the middle management

Here’s the complete Study

Download the study here:

The post Analytics India Jobs Study 2016: by AIM & Jigsaw Academy appeared first on Analytics India Magazine.

Humor cannot be detached from Business (or even technology for that matter), and business cartoons are the forefront of keeping humor alive. A great cartoon nails the issue at its head with those impactful 1-2 liners and helps bring the funny side up. The same is true for data science.

Yet, cartoonists are not the most celebrated lot. So, we decided to dig into the leading cartoonists from around the world, who are drawing get stuff on analytics for years. Here’s the list of 10 of those.

Cyanide & Happiness

A webcomic written and illustrated by Rob DenBleyker, Kris Wilson, Dave McElfatrick published on their website explosm.net. It was created on December 9, 2004, and started running daily on January 26, 2005.

The comic’s authors attribute its success to its often controversial nature, and the series is noted for its dark humor and sometimes surrealistic approach.

David Fletcher

David Fletcher (born 1952) is a New Zealand cartoonist. Fletcher was born in the UK but emigrated to New Zealand. He produces “The Politician”, a daily cartoon strip that appears in The Dominion Post as well as various publications around the world. He was employed as an illustrator and cartoonist by New Zealand’s largest daily newspaper the New Zealand Herald for three years, but for the last twenty years he has been working from home as a comic strip artist.

Apart from the “Politician” strip he also produces several weekly strips, “TV Kids” for the TV Guide and since 2007, Crumb, a strip created especially for mobile phones published by ROK Comics, which centres on the antics of an ever-hungry blackbird.

Jon Carter

Jon landed my first regular freelance job as cartoonist for a local weekly newspaper at the age of 18 and has been working neurotically on these ever since. The comic strip he has worked on the most is his panel “Cartertoons” (formerly “Funny Files”), which he began writing and drawing in the summer of 1989. It has appeared in a variety of magazines, newspapers, and web sites.

Cartooning is great because it’s such a pure outlet for aggression. If you come home at the end of a very bad day you can make a violent drawing and somehow things are okay again, even if only in small measure.

Marion van de Wiel

Born in 1963 in Goirle, North Brabant. She loves drawing above everything else, especially in a comic-like cartoon style. During her high school years she was always drawing her classmates.

A training as a fashion artist at the School of Art in Arnhem didn’t change that in any way. She became an illustrator and caricaturist. Hugo and she have been full-time caricaturists since 1992.

Mark Anderson

Cartoonist Mark Anderson lives in the Chicago area with his wife, their children, two cats, a dog and several dust bunnies. He had always loved cartoons, and had contributed to his high school and college newspapers. The creative outlet was exactly what he was looking for, and he began submitting cartoons to magazines. Working early mornings, lunch hours, and late into the night, he continued drawing, submitting, and building his website, Andertoons.com. Soon, Mark quit his day job to become a stay-at-home dad and draw cartoons full time.

Mark now runs Andertoons from his studio, where he sits at a drafting table. He lives in the Chicago area with his wife, son, daughter, dog, cats, and one well-used coffee table.

Mark Hill

A Denver area cartoonist and humorous illustrator, Mark began drawing when he was old enough to hold a crayon without also eating it. About 15 years later at the University of Illinois student newspaper, he was a cartoonist, columnist and editor. After college, Mark was a political cartoonist in the Chicago area…and then worked under contract for King Features Syndicate and Tribune Media, creating some now obscure comic strips.

Mark’s work has been published in over 100 magazines and newspapers, (including Time, Forbes and the Wall Street Journal), and has been featured on NBC’s Today Show.

Rob Cottingham

For 25 years, Rob Cottingham has helped senior strategic levels of government, business and advocacy organizations to engage with audiences in both the digital and offline worlds.

Pioneering in the online arena comes naturally to Rob, who launched his first web site in 1995. He built one of the earliest party leadership candidate web sites in Canada, launched the country’s first online political game, and oversaw the creation of an ambitious anti-tobacco website targeted to youth years before similar efforts became ubiquitous. He launched the ground-breaking Confeederation.ca site during the 2005-06 federal election, garnering national media attention for a service that aggregated blog posts from candidates across the country.

Rob maintains a long-running blog on technology and public affairs at RobCottingham.ca. He draws the popular Noise to Signal web cartoon, has been a regular freelance contributor to CBC Radio and performs in a variety of venues as a standup comic.

Scott Adams

Scott Adams does not need introduction, as the creator of famous Dilbert comic strip. His Dilbert series came to national prominence through the downsizing period in 1990s America and was then distributed worldwide. Adams worked in various roles at big businesses before he became a full-time cartoonist in 1995. He writes in a satirical, often sarcastic way about the social and mental landscape of white-collar workers in modern corporations and other large enterprises.

Ted Goff

Ted Goff has been drawing cartoons forever, and selling them since 1980. His cartoons have appeared in hundreds of publications around the world, and have been used in ads, presentations, T-shirts, newsletters, textbooks and posters.

Tom Fishburne

Tom started cartooning on the backs of business cases as a student at Harvard Business School. While in various marketing roles at General Mills, Nestle, Method and HotelTonight, Tom parodied the world of marketing in a weekly cartoon. His cartoons have grown by word of mouth to reach 100,000 business readers each week and have been featured by the Wall Street Journal, Fast Company, Forbes, and the New York Times.

Tom soon realized that cartoons are a remarkable form of shareable media. He launched Marketoonist to help large and small businesses such as Google, GE, Kronos, Motista, Rocketfuel, reach their audiences with cartoons.

The post 10 Humorous Cartoonists to follow if you are a Data Scientist appeared first on Analytics India Magazine.

Last year we took this initiative to identify Data Scientists in the country that are helping to push the industry forward. It is a grueling exercise of sorts; we invite nominations from various organizations, irrespective of size and nature of work. We also get in touch with data scientists that we know personally who might not necessarily be associated with an organization.

Then an expert team of leaders, past data scientists and evangelists are asked of their opinions on those nominations, on basis of parameters like pedigree, patents, competitions participated, any pioneering work etc. Yet, at the core of it, we look for that one significant consideration- what is the contribution to the industry and ecosystem in the country.

So, here we are with this year’s list. We have purposely kept the list different from last year’s (given the size of analytics industry in India). We hope the list brings you some inspiration and motivation to find bigger purpose in life.

Abhimanyu Dasgupta

Abhimanyu specializes in the design, development and deployment of data science algorithms in the financial services sector. An honors graduate in Statistics from the Indian Statistical Institute and on the cusp of being a designated Actuary with the Casualty Actuarial Society of USA.

He leads the FSI practice of Advanced Analytics professionals at Deloitte Consulting and has grown the practice from its nascence in 2006 when he’d joined as a rookie. His deep expertise in both analytics and actuarial science uniquely positions him as a key thought leader in the insurance analytics domain in US and India. As a young data scientist, he was instrumental in helping develop the first ever claims predictive model in US. This pioneering approach is a pending patent in his name. He is the only data scientist to be honored with Deloitte’s prestigious RMP award, an award that recognizes the top 1% consultants.

Abhimanyu is an active contributor in external forums including events organized by the IAI and ICADABAI where he has spoken on the advent of advanced analytics and its applications in various fields including Underwriting, Pricing and Claims Management. Among other roles, he also serves on the Advisory Committee on Analytics of IAI today.

Dinkar Sathe

Dinkar Sathe is Advisor, Analytical Consultant in SAS. He has around 26 years of diversified experience in quantitative analysis, problem-solving and research using statistical techniques. While studying at Purdue University he was tasked with optimizing the input levels required to create cost effective medicine tablets. After graduating with a MS Statistics degree, he started working for a leading Insurance company where he designed and developed a provider profiling system to identify potentially fraudulent or abusive patterns of medical practices. He was also instrumental in developing predictive models in the analysis of historical loss experience of automobile risks in order to identify risk characteristics that are correlated with losses. He worked in the Insurance industry for six years after which he worked for a leading credit card company in the USA where he became proficient in the credit card business.

He has gained a rich Marketing Analytics experience during consulting projects executed for a number of major corporations across the globe. Customer satisfaction has always been his foremost focus and aim. He has provided strategic consulting support to credit card program managers in areas of reporting, marketing plan fulfillment, measurement of effectiveness of past marketing efforts and in the identification of customer profiles for future promotional marketing.

Manish Gupta

Manish is an advanced analytics professional with experience in building & leading data science & analytics teams for developing competencies in customer analytics, real-time recommendation system, bigdata and advanced analytics solutions across various industries. Manish holds a PhD from IIT Delhi in the area of Machine Learning with more than 15 research publications in international journals and conferences with 1 US Patent which have more than 130 citations.

He recently joined American Express as Director, Global Credit Data Science and Machine Learning Research to carry out business specific data science and machine learning research and to establish machine learning best practice for business. In his most recent role, Manish served as Senior Vice President, Analytics at InfoEdge, parent company of Naukri, JeevanSathi, 99Acres, Shiksha etc.

He has also previously worked with Global Decision Management, Citibank as analytic lead. He has also work as Head (R&D) at Innovation Labs@24/7 Customer where he developed patented technologies for chat categorization and web acquisition engines. He has served country as Scientist at DRDO, a premier defense research organization in India. He received several awards including Scientist of the Year, Technology Award for his contribution to develop state of the analytics solutions for armed forces.

Nishant Chandra

Dr. Nishant Chandra leads the AIG Science R&D group in India where he develops natural language, text mining, and machine learning models for the insurance industry. He also directs the development of natural language platform and applications such as privilege text classification, contextual summarization, and conversational sentiment abstraction. Prior to AIG, Dr. Chandra has driven innovation in BFSI, e-commerce, R&D, and mobile telecom industries in USA and India. He developed and implemented natural language predictive models that are deployed in top banks and telecom companies resulting in $100M impacts across value chain. For his contributions, Dr. Chandra has received the prestigious Barrier fellowship and several other awards and recognition.

The Department of Homeland Security, United States Government has classified Dr. Chandra as an outstanding researcher. He was the conference session chair for GSPx conference at San Jose, California. He has been a reviewer for IEEE transactions, served in the editorial board of Human Language Technology conference, and speaker at several international conferences. He also has five assigned patents and several publications. Dr. Chandra is a passionate puzzler who invents puzzles and has represented India in the World Puzzle Championship at Stamford, Connecticut. He received his Ph.D. in Electrical and Computer Engineering from Mississippi State University.

Pradeep Gulipalli

Pradeep co-founded Tiger Analytics – an advanced analytics firm that builds data science solutions for businesses. He leads an interdisciplinary team of computer scientists, economists, statisticians, and business consultants. His team has done some pioneering work to bring data science into emerging business areas such as hyper-personalization, real-time decisions, dynamic learning, IoT and sensor networks etc.

Pradeep’s team has built several industry leading data science solutions. The early warning systems for railways they built are now the North American industry standard. Their algorithms that predict emerging global technology trends guide M&A decisions. Their first of its kind interactive algorithm framework involving Dynamic pricing, Optimization, NLP, and Game theory helps place hundreds of millions of digital ads across devices every day.

Pradeep has practiced data science for more than a decade architecting solutions for several Fortune 100 enterprises. In addition, his work around mathematical modeling of cities – socio-economic activity, transportation networks, land use, air quality etc. – has won him international recognition for application of data science to public policy. He has published in leading journals, co-guided research theses, and collaborates actively with universities. Pradeep holds a B.Tech from IIT Madras and an MS from The University of Texas at Austin.

Sandhya Kuruganti

Sandhya Kuruganti is a senior analytics leader in the Indian banking industry with more than 20 years of experience. She holds a Master’s degree in Economics from the Delhi School of Economics, India and a Doctorate in Economics from Rutgers University, USA. She has been one of the evangelists who has pioneered the usage of decision sciences for banks in India, as early as 16 years ago, when the analytics practice in India was still nascent. Sandhya’s thought leadership in developing and implementing advanced analytical strategies for customer life cycle management has been a key success factor for corporates that have leveraged her expertise in successfully embedding analytics in their business practices. As Senior Vice President at Citibank, she set up, nurtured, and led the Business Analytics function for Citibank India and the Regional Analytics Centre of Excellence for Asia Pacific.

As a management consultant, she is currently on a mission to build analytics culture at Public Sector Banks, and educate the budding data science community in India. She also conducts applied analytics training programs and has co-authored a book titled “Business Analytics: Applications to Consumer Marketing – Sandhya Kuruganti and Hindol Basu”, published by McGraw Hill India in March 2015.

Sarita Digumarti

Sarita Digumarti is the COO & Co-founder of Jigsaw Academy, a premier analytics training institute. A specialist in Analytics, Consulting, and Outsourced Services and Management, Sarita is all about numbers. Holding an MBA in Finance followed by an MA in Quantitative Economics from Tufts University, Sarita has spent over 15 years working as a data scientist helping clients across diverse sectors including retail, healthcare and financial services, both in India and the US.

Sarita now focuses on both education and new frontiers in the data science space. She is an expert educator who trains students at Jigsaw Academy, the participants of the Executive Program in Business Analytics (EPBA) at MISB Bocconi, Mumbai, and the numerous corporate employees whose companies have partnered with Jigsaw Academy. In recognition of her professional excellence, Sarita has been presented with the Global Achiever’s Award for Educational Leadership by the Economic Development Forum.

Sudalai Rajkumar

Sudalai Rajkumar (aka SRK) is the Lead Data Scientist at Freshdesk, responsible for developing scalable machine learning / data science systems for the organization. Prior to this, he was part of the R&D team in Global Analytics doing machine learning research and then moved to Tiger Analytics, where he has solved a lot of interesting data science problems for various customers across the globe in multiple domains including finance, e-commerce, online advertising, health care, transportation, retail. He has worked on varied problems ranging from doing simple analysis on structured data to natural language processing and voice analytics in his career.

Apart from his day job, he used to take part in various data science competitions to enhance his knowledge and has won several of them. He is one of the top 25 data scientists in the world in Kaggle. He is one of the top solver in CrowdAnalytix platform as well. He has published few papers in International conferences and also has a patent filed under his name.

SRK received his Bachelors degree from PSG college of Technology and got his executive certification in analytics from IIM Bangalore.

Tuhin Chattopadhyay

Tuhin Chattopadhyay is an eminent business analytics and data science thought leader with a progressive and proven track record of twelve years of experience in academia and industry.

Tuhin has a profound knowledge of the marketing domain besides being an analytics expert. Academically, Tuhin is an ISB trained business analytics professional and IIM Ahmedabad educated management expert. A double masters in science (M.Sc.) and business administration (M.B.A.), he holds a Ph.D. in management (marketing analytics). Technically, he is a SAS certified predictive modeller and certified for IBM business analytics software. Prior to joining the data science industry, Tuhin was a renowned professor of business analytics and taught at a number of reputed B-Schools for a decade.

As an academician, Tuhin pioneered the introduction of analytics based subjects into the curriculum of multiple B-Schools. Tuhin is a prolific researcher and has authored research-based books. He has more than thirty research publications in refereed journals and conference proceedings. To train the corporates as well as faculty members of B-Schools, he regularly conducts workshops on business analytics. He is the Editor-in-Chief of International Journal of Business Analytics and Intelligence and is the editorial board member of a number of leading journals.

Viral Shah

Viral Shah is a co-creator of Julia and CEO of Julia Computing. Julia is a modern and easy to use high-performance programming language for data science. It provides the performance of C++ while being as easy as Python, R, Matlab, and SAS. While it is an open source project with a diverse community of almost 500 contributors around the world, research on Julia is anchored at MIT. Today, the Julia community counts over 500 contributors and over 1,100 community contributed packages. Julia is being used by a number of universities for teaching and research, and by businesses in areas as diverse as engineering, finance, manufacturing, healthcare and retail.

Apart from Julia, Viral is also co-creator of Circuitscape, an open source program for ecological conservation. Prior to that, he worked in the Aadhaar project, leading the design of Aadhaar-based eKYC, subsidies and payment systems. These experiences are captured in Rebooting India, a book he co-authored with Nandan Nilekani. Viral has a Ph.D. in Computer Science from the University of California at Santa Barbara.

The post Top 10 Data Scientists in India – 2016 appeared first on Analytics India Magazine.

Blockchain is the most trending technological term today and is forecasted to disrupt various industries in coming future. The buzz is building up in India as well. Just last week, Mahindra and IBM joined hands to develop Blockchain Solution for Supply Chain Finance. Also, mid of this year, ICICI bank adopted Blockchain in a push for digitalizing banking.
Yet, in the startup space, the activity seems to be relatively muted in India when compared globally. One reason can be that the technology is still in its early days and yet to mature. We presented a tutorial on Blockchain earlier this year.

So, we decided to compile a list of some interesting startups in this space in India. Here they are:

Auxesis

Auxesis is a technical innovation company with advanced competency in FinTech and Blockchain Technology. Working for the development of new innovative technological solutions by providing a balance between technology and economic sustainability for the start-ups. Working for businesses to help them focus on their best what they do without the worry of their technical development and maintenance. Currently, company is working with IIT Bombay Entrepreneurship cell & European Incubator (SEED) start-ups projects from idea until there transformation Phase.

Auxesis is constantly experimenting in collaboration with Blockchain Lab, London to find better use cases for other industries. Their current initiative includes Banking, Remittance, Insurance, Event and Ticketing industry while Pharmaceuticals, Luxury goods, Gambling are under consideration.

Coinsecure

Coinsecure was built with the motto of “Connecting India to Bitcoin”. Today we focus on “Building an entire eco – system for Bitcoin and Blockchain in India” and we have been able to accomplish this with the array of products and services that we offer. Apart from being the largest and best Bitcoin exchange, Coinsecure offers a Bitcoin wallet, A Blockchain solution (the Block explorer), merchant gateway, comprehensive API’s and a testnet service.

Coinsecure intends to be the one stop shop for all Bitcoin and Blockchain related needs and accomplishes them through several partnerships across the globe. Coinsecure is India’s first and only Bitcoin company to have secured the highest funding round with 1.2 Mill USD and have been at the fore front of developments which are user friendly and secure!

EzyRemit

EzyRemit is an innovative FinTech and Blockchain platform and solutions company which is focused on changing the remittance market. Working at the intersection of technology, payments and banking industry from many years, we saw the many redundancies in the system and the need for change.

A glimpse of the solution came with the appearance of Bitcoin and later Ethereum, offering different blockchain technologies we could work with to find our solutions. By pairing experience from the Payment, business process management & software application development, founders use blockchain technology to empower people around the world with free access to a global financial system & faster transaction.

Hashcove

HashCove is a Blockchain Products Company that helps evaluate, validate, design and create end to end solutions in Blockchain.

HashCove is the Blockchain arm of uTrade Solutions. uTrade is a FinTech company providing multi-asset trading platform,low latency algos engine, risk management and Blockchain Solutions.

Their products are widely used by global financial institutions including Brokers, Algo Firms, Forex Traders, Stock Exchanges and their end customers.

KrypC

KrypC is a FinTech solution and service provider focusing on bringing innovative solutions in the field of Blockchain & Digital Currency. Currently the team is developing and deploying industry use cases on the blockchain framework which would help organizations to integrate current processes to the DLT network.

The goal of KrypC’s technology services is to help businesses understand the power and utility of Distributed Ledger Technology (DLT), assess the potential areas of application, provide technical framework & design and effective implementation of the technology solution.

With vast experience in Digital Signature Certificates, Security and Cryptography, KrypC has multiple intellectual property in the areas of Mobile Wallet, Digital Currency, Security and Payments solution.

MindDeft Technologies

MindDeft was founded with the thought of making difference with IT services and providing the quality services to customers. Since our inception, the goal is to deliver solutions to complex problems that really makes difference to the customers.

Apart from traditional technology stack, the team is very much open for adopting the new trends and technologies that is really making difference to solutions they are providing. Blockchain is one of the trend that they love, and they are in the progress of taking expertise of various Blockchain tools for example, Ethereum and HyperLedger. They have been doing extensive research for Blockchain and delivered the proof of concepts for various domains on how to implement the smart contracts with Blockchain tools.

Trestor

Trestor is an India’s first blockchain startup, which has created “Trest” a secure, digital, store of value. Using the power of Trestor’s blockchain and their decentralised network of trustless nodes ‘Trests’ can be transferred directly from person to person anywhere across the globe. There is zero transfer fees, Trests are transferred within seconds, accounts can never be frozen and there are no paperwork or limits!

Trestor network is designed keeping under banked in mind. Most think that poor just need a bank account and a payment card and once they get it, they will happily switch from cash to a centralized, freezable, chargeable, hackable payment system that the rest of us use. Maybe we should re-think this assumption. “Maybe what unbanked people really need is digital equivalent of cash instead of our banks and our way of banking”.

The post 7 Startups in India working on Blockchain Technology appeared first on Analytics India Magazine.

Plan on upskilling in Data Analytics? Now, you can become a post graduate in Data Analytics with a Diploma from one of the premier technical institutes in India – International Institute of Information Technology Bangalore (IIIT-B) in conjunction with ed-tech startup UpGrad.

Co-founded by entrepreneur and media stalwart Ronnie Screwvala and three education experts – Mayank Kumar, Phalgun Kompalli and Ravijyot Chugh – UpGrad is an online education venture offering rigorous, industry-relevant programs for working professionals who plan to constantly upskill themselves to remain relevant. A recent entrant to ed-tech ecosystem (started in 2015), UpGrad’s USP is bringing academia and industry veterans together for the best learning experience.

That’s exactly what the super-intensive, online Data Analytics program offers. With a duration of 11-months, the online Data Analytics program build its credibility with faculty from IIIT-B, a well-renowned technology institute in India, and the best minds from the analytics industry (Cognizant, Genpact, Flipkart, InMobi among many others).

Here are some of the key facts of the program which is still underway:

• Duration: 11 months
• Medium: Online
• Fee: Rs 2 lakh
• Number of students enrolled: 300
• Faculty: Industry Leaders and IIIT-B faculty impart instructions
• Choose Domain Specialization: Students can choose from Banking, Healthcare & E-commerce Analytics electives, in the second half of the program
• Tools: You will learn how to use tools like Spark, Tableau, Hadoop and programming languages R, Python, and MySQL
• Placement Support: Students get career assistance and facilities such as Resume Review and Interview Preparation
• 3 month Capstone Project: Get hands-on experience in driving insights from data for businesses
• Next batch: Starts in March 2017 (Applications open now)

Curriculum

Industry veterans (leaders from Uber, Cognizant and TataiQ) among others and top academicians from IIIT-B have created this syllabus combining a set of guidelines — from deep understanding of how data analytics spurs business decisions to the ability to develop predictive modelling solutions to support that decision making process. Broadly put, the program imparts instructions in Data Management, Statistics, Machine Learning and Big Data.

The program prepares students for careers in analytics, predictive modeling, business intelligence and data mining in data-intensive industries such as Banking, Healthcare, and E-commerce, among others. The 11-month program tackles the fundamentals of Statistics and Data Management, Data Sets and Data Models, Business and Data Understanding, Data Warehousing, Data Visualization, Predictive Analysis and Big Data Analysis. The program can be divided in two components — first six months focuses on fundamentals of Data Analytics and second half is Domain Elective, depending on their career objective, students can choose from BFSI, Healthcare, E-commerce or Retail/FMCG.

What you learn at UpGrad:

• How can you build a successful career in analytics by understanding the academic concepts and industry applications?
• What are the correct statistical and machine learning methods that will produce the best results for an organization and which method to use in which scenario?
• How to combine statistical knowledge, technical know-how and business domain to become a data driven leader?

Domain Specialization

Since the program is designed in a way that it is industry relevant, it has concrete application to some of the most data-intensive industries in India such as Banking, Healthcare, E-commerce, Retail and FMCG among others.

“In the Indian eco-system, these industries are the biggest consumers of data and the data DNA already exists within them. For example, an FMCG firm may require a data analyst to optimize logistics and warehousing. An e-tailer might want to know the online preferences of his customers. Within the program, there are a wide range of such case studies where students can learn how to apply analytical methods in real-life scenarios,” shared Rohit Sharma, Program Director, UpGrad.

The first half of the program covers the fundamentals and the advanced data science techniques and the second half covers domain electives. Here – the students can choose whichever domain they wish to focus on.

Some of the case-studies are

• Uber Supply Demand Gap:
• In this case study the students use analytics to identify why Uber sometimes faces a supply-demand challenge and what can be done to overcome it.
• Telecom Churn Prevention: It is one of the most competitive sectors and the existing players face the challenge of customer churn (opting for other service providers). The students learn to use machine learning to identify the cause of customer churn.
• E-commerce Market Mix Modelling: Understanding the types of customers in the target group e-tailers target their marketing effort. In this case study the students learn to use modelling to figure out the optimal spend across channels to drive sales.

Placement Prospects

Strong industry network of UpGrad and IIIT-Bangalore should be helpful in creating attractive job opportunities for the students. Since the program is in its maiden run (the first batch is still underway), it is too early to comment on placement. An important point to keep in mind is no learning institute offers 100% guarantee. However, UpGrad provides placement assistance and has developed an active industry partner pool (Gramener, Genpact, Uber, Fractal Analytics, Cognizant) to bolster recruitment.

The popularity of the program can be gauged by the sheer number of students who enrolled – 300.

But how does one measure the effectiveness of this online program, given that it’s in first edition? According to Rohit Sharma, the director of the program, “In online courses, the massive problem is dropouts. We have a 96% completion rate, which means the program is highly successful. Our focus is on providing a seamless learning experience”.

Last Word

Pros:

1. One of the best high points of the program is IIIT-B (which has a great faculty) that brings credibility.
2. UpGrad is providing a PG Diploma from IIIT-B as opposed to Management institutes and learning centres that provide a certificate.
3. The Rs 2 lakh fee is comparable to what is charged by learning centres or business institutes.
4. UpGrad is providing Career Assistance which most learning centres do not.
5. In order to build a student community & peer support mechanisms, UpGrad has its own Discussion Forum where students are encouraged to interact and debate out their doubts or challenges. Also, UpGrad conducts an offline meetup every month in Delhi, Bangalore and Mumbai where one industry speaker and one faculty member are present.
6. Lastly, the program is a great combination of academic and industry experience

Cons:

1. It’s a recent entrant in ed-tech ecosystem (UpGrad started in 2015), hence other start-ups already have a head start on them.
2. The first batch is still underway and we can’t comment on any Placement Prospects

All in all, UpGrad is a good stepping-stone for upgrading the Data Analytics skills as it capitalizes on IIIT-B’s esteemed faculty and the rich industry experience of leading minds from the analytics field.

Moreover, in our recently published top course ranking, this course is the lone online-only course in India to make it to top 10. Also, remember that a diploma from IIIT-B is a no mean feat to achieve. You can apply for the program here.

The post In Review: Ed-tech Start-Up UpGrad’s & IIIT-B’s P Diploma Program in Data Analytics appeared first on Analytics India Magazine.

Analytics is by far the biggest influencer in IT industry – a phenomena evident by the rise of next-gen technology Cognitive Computing, Blockchain and Virtual Reality which has at its core a valuable asset “data”, and analytics quite irrefutably is the essence of it. After all, it’s the whole mix of technology, data and analytics that is revolutionizing the way we work.

In keeping with our annual tradition, we present the much-researched and a carefully thought-out study carried out in association with AnalytixLab, a premier analytics training institute in India. We invited nominations from various organization to identify the analytics trends that will shape the future of our industry in India for 2017. After a lot of fact-finding from the industry insiders, we bring to you the top 10 analytic trends that have had the highest impact on the analytics industry today and potentially going forward in 2017.

This year, we received an astounding 36 submissions. A lot of new trends have hit the space, a few faded away and there are others that endured and will definitely stay. This study, now in its third year presents neatly sorted out viewpoint of the industry leaders and veterans on Top 10 Analytics Trends in India to watch out for in 2017.

Artificial intelligence becomes pervasive in business

The Cognitive age is clearly upon us— it is indicated by the fact that more than $1 billion in venture capital funding went into cognitive science in 2014 and 2015, and further supported by fact that various analysts project the overall market revenue for cognitive sciences to exceed $60 billion by 2025. As the cognitive era evolves, it will likely become another key decision making tool in the toolbox of CXOs; vital for the right applications but not entirely replacing traditional business & advanced analytics capabilities that complement the human thought process. In a nutshell, the man-machine dichotomy is not “either-or”, it is unequivocally “both-and”.

Debashish Banerjee, Managing Director at Deloitte Consulting

There is a lot of hype around “artificial intelligence,” but it will often serve best as an augmentation rather than replacement of human analysis because it’s equally important to keep asking the right questions as it is to provide the answers.

Souma Das, Managing Director, India, Qlik

Analytics of Things continues to be a game changer

Internet of Things and Analytics of Things: The Indian Internet of Things (IoT) market is set to grow to $15 billion by 2020 from the current $5.6 billion. Just about every type of company seemed to have an IoT strategy in 2016. However, today, IoT is more about Data Than It Is Things. The original description of “Internet of Things,” was describing a network of connected physical objects. But in 2016, it was apparent that this initial description didn’t consider the importance of data or cloud computing. So now, IoT isn’t about connecting billions of objects to the Internet, it is really going to be all about the data and the ability of organizations to gain insights out of all of this data. This means that getting value from data goes beyond devices, sensors and machines and includes all data including that produced by server logs, geo location and data from the Internet.

Sunil Jose, Managing Director, Teradata India

The “Internet of Things” is exploding. It is predicted that the number of device connected would reach 50 billion by 2020. Most of these smart devices would be in factories, energy sector, health care systems, home appliances and wearable devices. The vital data so generated would enable us to track health parameters, optimise machine performance, and reduce response time for breakdowns and also save lives.

In order to create real value of IoT/IIoT, the Sensors & Communications node needs to integrate with the Analytics infrastructure, else it will be a simple data collection exercise. The technologies & skills related to IoT protocols, edge analytics & real time sensor analytics will be the key differentiators for its success & adoption in the market.

Vinay Gupta, Head of Analytics at Suzlon

Enabling real-time automated decisioning systems

There is a visible shift across many big data and analytics users to streaming and real time analytics. Businesses particularly digital advertising, ecommerce, logistics & transportation are looking to leverage ream time analytics and are heavily invested in this space. This is also apparent from the elevated adoption levels of Apache Spark Streaming, Apache Storm or Twitter’s Heron.

Srikanth Sundarrajan, Principal Architect at InMobi

Enterprises are increasingly enabling real-time automated decisioning systems whether to streamline operations or mitigate risk. The older rule-based systems are now being replaced by a new generation of systems powered by online machine learning and artificial intelligence – these are self-learning systems that can recalibrate in an automated manner and can be deployed on a large-scale.

Pradeep Gulipalli, Co-founder at Tiger Analytics

Analytics is made invisible, embedded within the system

Analytics works best when it’s a natural part of people’s workflow. In 2017, analytics will become pervasive and the market will expect analytics to enrich every business process. This will often put analytics into the hands of people who’ve never consumed data, like store clerks, call-center workers, and truck drivers.

Deepak Ghodke, Country Manager, Tableau

Technologies that nudge us to drink water, take regular walk breaks, or inform us that our cab has arrived have become commonplace. This is not restricted to consumer facing decisions. In fact, businesses in 2016 are beginning to realize the value of this kind of data and deploy on-demand analytics to drive better decisions. They are capturing and streaming unstructured data, blending it with other data sources, deploying analytical models to unearth insights, and are using rules engines to drive applicable “nudges”.

Mihir Kittur, Co-founder, Ugam

Fintech is growing and so is Fintech Analytics

Going by the events of the last year (2016), Fintech will clearly emerge as the most challenging as well as beneficial. The linkage of various identity proofs to uniquely identify a person and their financial footprint would be the key to the mission to drive out corruption and black money.

Dr Nupur Pavan Bang, Associate Director, Thomas Schmidheiny Centre for Family Enterprise, Indian School of Business

Financial institutions are moving rapidly towards “digitization” and educating their customers to adopt digital channels for day-to-day activities. With eroding revenue streams, intensifying competition, and ever-increasing customers’ expectation financial institutions need to explore new way of doing business. Measuring customer relationship, evaluating the customer journey and recommending right bundle of product & services at the right price in real time through technology enabled digital platform will be the vital enablers to improve customers’ banking experience.

Suman Singh, Chief Analytics Officer, ZAFIN

Rise of Self-Service Analytics

The realization that what delivers impact is not automated MLR or one-size fits all solutions, but context driven customized solutions that leverage business know-how (that probably exists deep within a company) and domain knowledge (of expert consultants with rich industry and applied analytics experience) will dawn on most business leaders in 2017.

Randhir Hebbar, Cofounder at Convergytics

Technological advancement has led to tools for Self Service Business Intelligence or Self Analytics. This approach meets the needs of data producers and consumers alike, adding speed and agility to the process while protecting organizational data and the system overall with a single version of the truth.

Tejinderpal Singh Miglani, CEO, Incedo Inc

Democratization and consumerization of analytics

More organizations are “democratizing” Business Intelligence (BI) and analytics to enable a broad range of non-IT users, from the executive level to frontline personnel, to do more on their own with data access and analysis via self-service BI and visual data discovery such as drag-and- drop dashboards.

Anil Chawla, Managing Director, Customer Engagement Solutions, Verint Systems

More data is now available to companies of all sizes. So more easy access to data within a company & sources to find external data will be a trend that I see. Companies can partner each other & leverage each other’s data. A DTH company knows when you move residences & that data can help a Retailer who sells furniture or is in that catchment. I believe that in year 2017 more marketers will leverage each other’s data to build more effective analytics solutions.

Ajay Kelkar, Co- founder of Hansa Cequity

Mobile first Omni channel strategy

With increasing penetration of mobile phones, the number of mobile apps have sky rocketed. Due to the limited space on these mobile phones, consumers are engaging with apps that create value for them. To stay relevant, the apps developers are using app analytics to understand their users’ profile and transaction behaviour to fine tune their product features and offering to increase user experience and engagement. We will continue to see analytics playing a larger role to declutter the space.

Debasmit Mohanty, CEO & Founder, StratLytics

With increased penetration of mobile, companies are taking a Mobile First approach to engage consumers, leading to progress in Mobile Analytics. With the available location and motion sensing capabilities, significant progress was made in data collection in a privacy compliant fashion to determine what, when, where, and why of the activities that consumers engage in. This data is enabling improved insight and reach for business growth and consumer experience improvement.

Amit Deshpande, Vice President, Analytic Consulting Group, Epsilon

Leverage GeoSpatial analytics in improving business models

In 2016, we have seen “Geo spatial analytics” gain good amount of momentum in India. Geo spatial analytics refers to mapping of events to point in time locations.

With a massive adoption of mobile devices across India in the past year and businesses getting more digitized, there are lots of “time and place” data that is being collected today. Besides mobile devices, emergence of sensors with respect to smart cities in India, drones being evaluated in agricultural and construction sectors, emergence of social media in real time, we are seeing tremendous business potential in terms of leveraging Geo spatial analytics in improving business models and this trend will only continue to grow.

Sunil Shirguppi, ‎Senior Vice-President – Big Data and Analytics at Happiest Minds Technologies

In 2016, businesses found value in understanding the ‘where’ factor of data and the ability to query location based information or location analytics into their existing analytics. Business intelligence solutions along with geographic analysis brought forth insights that helped companies better communicate with their customers, create more targeted promotions and pursue previously unrecognized cross-selling opportunities.

Manish Choudhary, SVP, Global Innovation and MD, Pitney Bowes India

Analytics Governance Platforms

The use of analytical models is significantly increasing across business functions (marketing, sales, risk, pricing, etc.) and business & product lines of the enterprise. They are at different stages of deployment and being used continuously by various business users simultaneously. However post implementation of such models, businesses are not necessarily being able to track how these models are performing and not sure if they are delivering the promised value. Neither do the businesses know the interlinkage effect of all these models working together.

With the increasing use of analytical models in business decisions, consolidating them in a single technology console, monitoring the health of analytics implementations and model performance is becoming a crucial need for business leadership and regulators. This will help track the analytics performance, demonstrated ROI and avoid the risk of incorrect decisions made because of analytics. ‘Analytics governance platforms’ will gain prominence across all large enterprises and will become mainstream to monitor the analytics deployment through workflows. The ROI of the analytics governance platforms will be seen in the long-term and would reap the benefits of governing complex analytics environments in a single platform with more visibility to the analytics ROI.

Prithvijit Roy, CEO, and Co-founder, BRIDGEi2i Analytics Solutions

Here’s the complete report.

Download the complete report below:

The post Top 10 Analytics Trends in India to watch out for in 2017 – By AIM & AnalytixLabs appeared first on Analytics India Magazine.

What forms the core of businesses today? Huge volumes of data that flows in and out every day — and though it does matter, what comes into play is the ability to use data and models to make better business decisions. UpGrad, a leading edtech startup, have collaborated closely with Uber for their Data Analytics course content generation.
Sai Alluri, Analytics Lead at Uber India talks about how supply positioning model, segmentation and visualization tools that are applied at Uber and how Uber stays on top of the game plan by understanding the biggest mismatch between supply and demand.

From predicting the future to preparing for it, we list down top three reasons why this one is for keeps:

• Get a peek into how Uber analyses historical data, uses it as a benchmark and predicts future action
• Get pointers from the best in the industry, Sai Alluri part of Uber’s PRO team
• Learn how to leverage analytics to stave off competition

Supply Optimization at Uber

The supply positioning model at Uber refers to anticipating demand patterns, and placing driver partners across those hubs with the aim to plug in the demand, lower ETAs and increase overall efficiency. One of the key focus areas is moving from a passive supply-positioning model to active through specific recommendations across the network.

How is supply positioning done at Uber?

In the words of Alluri — Supply optimization is one of the biggest focuses at Uber and the challenge is to efficiently manage optimizing the supply wherever there are high areas of demand can be. One of the methodologies is through searchsurge — in real time, meaning that supply comes in from the highest area of demand. Say for example, when you see a search surge multiple in 2x or 3x, it portrays how much demand is in that particular area and what supply do you need to meet this demand.

Building models based on historical data

Uber analyzes historical data for say, last three or four weeks and identifies pockets within the city that witness extremely high demand. Let’s keep Gurgaon as a case in point. “Say there is a high search multiple in Connaught Place and our driver partner is in Gurgaon which is X kms from CP. It is very difficult for a driver to move from Gurgaon to CP given the traffic conditions and it might take him longer to reach. How do we know in advance where this demand is going to be based on historical data?” shares Alluri.

Here are some of the key steps on how the model was built:

• Look at historical data for the last three or four weeks
• Look at the time, day and specific areas within the city where the highest demand comes in
• Key metric is specifically the number of requests coming in and how many are getting completed in different pockets of the city
• If a specific pocket has a really low completed trips request, it implies a high demand in that hub but not enough supply
• Next step is to focus on how to proactively tell drivers to move within these areas not in real time but a 2 hour or 3 hour lag so that they can position themselves there when the demand arises

Supply Positioning in a Nutshell

How Uber does supply positioning is by specifically a) breaking down the city into multiple pockets, b) then identifying these pockets based on the demand parameters that show up, c) once you identify these pockets, you can figure out how you want to position the supply chain in these specific areas.

“Say for example, a specific pocket has a low complete request ratio or has fewer number of rides completed as compared to other areas, what should be done is ensuring how to get drivers get in the demand hub in time,” says Alluri.

Key parameters addressed for the analysis are: broken up by hour of day, by day of week and by specific pocket.

Meeting the Demand Supply Gap with Predictive Analytics

So now that you have the information, how do you use it to inform future decisions? In case of Uber, the real challenge is in filling the demand supply gap. “The idea is to figure out if the highest area of demand is in one specific pocket but the supply is going to come in from a different pocket. Which means we need to send this message to driver-partners early so that they can get to this specific area and ready to go when the demand hits,” points out Alluri.

At Uber, this analysis is automated to drive the following results:

• Uber sends out weekly communications to drivers at real time
• Weekly communications inform about high demand areas, with specific recommendations
• Enabling driver-partners to make best decisions, increase earnings and lower ETAs

Objective of historical analysis – build forecasting model

Alluri informs that the idea behind analyzing three-four weeks of data for a specific city, further broken down into specific hub/ pocket within the city and by hour of day and day of week is to get consistent behaviour across that time period for that particular pocket. The motive is to set a benchmark and rule out weekly anomalies. And it is further used to build a potential forecasting model where one can predict the highest demand or lowest supply and keep modifying it on a weekly or bi-weekly basis as the data changes.

A/B Testing & Clustering/Segmentation Analysis

At Uber, the goal is to drive efficiency across all areas of business. A/B testing was to find the most optimized and effective communications that had to be dispatched to driver-partners to address their issues, convert drivers to become loyal Uber partners by incentivizing.

“We want to make the process for a driver-partner signing up on our platform easy and scalable, so that they can reach out to us for specific issues, such as using the app. For example, as soon as the driver becomes active on our system, we want to make sure if he has any questions pertaining to how do you go online or how do you essentially go pick up your customer. So we monitor every aspect of this journey map at different cycles,” says Alluri.

The communication dispatch was targeted at converting drivers into loyal Uber partners. An A/B test was set up for two specific cohorts of drivers who had joined in the same week. Let’s keep a 100 drivers in cohort A and another 100 in cohort B.

• Idea is to find out how many don’t take the trip in the first 3-4 days
• Reach out with specific communications to drivers who still haven’t gotten activated
• Did the communication improve efficiency and drive conversions vis a vis cohort B that did not receive any messaging

The goal of A/B test was to use resources, in this case communications and incentives effectively:

• Lift conversions, urge drivers to become activated and turn from part time to full time
• Find out what communication is most (text or more personalized calls) effective
• Find out what should the content be and how to build the iterative process

Clustering Analysis basically means breaking up huge data sets into further subsets to help get better insights into critical decision areas. “What happens with clustering/ segmentation analysis is that, it is an iterative process, you keep building into the model and keep finding data sets so that you gain smarter and stronger insights,” notes Alluri.

In this case, segmentation was based on hours and trips. Alluri shares how the model was further optimized to include trips and how it led to increased revenue for drivers. “When we started this model initially it was meant as a question analysis and we used hours, that driver partners were putting in on a weekly basis or a daily basis as a variable. But as the model became smarter we wanted to include trips also to ensure that drivers that are driving at night or just part time at night are not coming online for just 4-5 hours but are able to get trips, end result being they are engaged on our platform,” he says.

End result was:

• Helping part time drivers find trips at night (we don’t want a driver coming online at a wrong time)
• Achieve their running target, thereby meeting revenue generation
• Boosting loyalty, converting from part time to full time (achieving day-time trips as well)

SQL still triumphs in Data Analytics

“Data warehousing is set in a way that we can do analysis on it, so it is easy for city teams and analysts to go into this data, get what you need to figure out what the biggest problems/ issues are in those specific areas and how to go about fixing it,” explains Alluri.

Alluri tells why SQL in preferred in Analytics

• There are no manual mistakes
• Write the query you want, find out what information you need and run the logic in that query
• When you get the file you are ready to share and you can also keep adding analysis on it
• Automate it using either R or. Python and gather information sets that are more useful

Visualization at Uber

Visual analytics is used at Uber to make data look more actionable and understandable. In India, one of the tools used by Uber’s city teams is heat maps which is used to find out where exactly is the biggest mismatch between supply and demand. Our team uses visualization layers on most business insight applications and uses it to find out the sequence of data flowing in.

About Sai Alluri:

Sai Alluri holds a degree in Mechanical Engineering from University of Illinois at Urbana-Champaign. He worked in consulting before joining Uber in San Francisco, California. He shifted to India last year to set up a team and focus on operational and analytical challenges in India.

The post How Uber uses data analytics for supply positioning and segmentation appeared first on Analytics India Magazine.

In 2016, Analytics India Magazine visitors gravitated toward new trending technologies such as Artificial Itelligence, Blockchain, Artificial Intelligence and Chatbots. They also showed significant interest in articles related to analytics careers, salaries and education. But the most popular new article by far was about a timeless topic: analytics companies and startups in India.

1.    10 Emerging Analytics Startups in India to watch for in 2016

Here are 10 fighters for the year 2016 who have not only shown tremendous growth but also hold huge potential for their future.

2.    10 Analytics firms in India you wish you worked for – 2016

We bring to you the list of 10 companies that makes it to the most desirable analytics firm you wish you worked for! The list has been chartered after a thorough research conducted in the analytics community and lists 10 most popular and talked about analytics providers from India.

3.    10 startups that are changing the face of Virtual Reality in India

We approached virtual reality startups in India and came up with 10 start-ups that are changing the way humans interact with computers and enriching the real world with an unrealistic approach towards videos, sound, graphics and more.

4.    Top 10 Data Scientists in India – 2016

List of leading data scientist in the country.

5.    10 Most Influential Analytics Leaders in India – 2016

Here is the list of Top 10 Influencers in the Indian Analytics Industry.

6.    Analytics India Salary Study 2016 – by AnalytixLabs & Analytics India Magazine

An initiative by Analytics India Magazine in partnership with AnalytixLabs, the focus of this study is to compare the salary trends amongst professionals, specializing in different skills and tools, while also providing an overview of the analytics salary in key Indian metropolitan cities as well as companies and across various experience levels.

7.    Top 10 Analytics Training Institutes in India – Ranking 2016

As part of the annual ranking process, Analytics India Magazine, brings all the aspiring Data Scientists this year’s ‘Top 10 Analytics Training Institutes in India’. AIM has been conducting this ranking for four years now and has successfully provided insights into the analytics education world.

8.    10 Startups in India that are leading the race of Artificial Intelligence

Analytics India Magazine presents you a list of ‘Top 10 Artificial Intelligence Start-ups’ and how each one of them is positioned differently catering to a niche area of artificial intelligence.

9. Top 10 Analytics Courses in India – Ranking 2016

After 2 long months of in-depth and rigorous study, Analytics India Magazine is out with its annual ranking for ‘Top 10 Analytics Courses in India’. This being the fourth in the row, AIM has always been positively supporting its aspiring data scientists by providing them insights into the world of analytics education.

10. 10 TED Talks on Analytics that you don’t want to miss!

We decided to bring some of the best TED Talks (old & new, organized according to views and popularity) in which Analytics was the core topic of discussion. You will see how people from different domains have integrated analytics in their work!

The post The 10 Most Popular Analytics India Magazine Articles of 2016 appeared first on Analytics India Magazine.

The post 2016 was the year of Chatbots, and it’s getting bigger appeared first on Analytics India Magazine.

South East Asia has got it’s first Blockchain Incubator and mentored/invested by Bitcoin Pioneers like Roger Ver, Amit Bhardwaj, Michael Terpin to name few.

The Incubator is very aptly named Satoshi Studios and is inviting applications from entrepreneurs working in South East Asia region. The incubator has a 3 month intensive residence program in New Delhi India, where the founders will spend time with the other Blockchain startups and receive 50K USD in funding for 8%-15% equity in the company.

In our conversation with the Incubator’s co-founder, Sahil Baghla, said, that ”we had been receiving a lot of interest from fellow entrepreneurs seeking feedback on their ideas using the Blockchain technology and some of the entrepreneurs we’ve met are building some really interesting applications, and we wanted to work with them and see if we could get an opportunity to know and work with more of these geniuses”.

Speaking further, Sahil said, “We are proud to be backed by people like Roger Ver (who has single handedly funded the seed rounds for the entire first generation of Bitcoin businesses), Amit Bhardwaj (who is leading the march for Bitcoin Adoption in India), Michael Terpin (he co-founded BitAngels, the first angel network for investments in bitcoin/digital currency companies) and more Bitcoin Pioneers.

Speaking about what else, the incubator will the offering to the selected startups, Sahil said “ Startups will get an awesome workspace and living space for 3 months in New Delhi. There will be intensive mentorship sessions by Blockchain veterans (including our backers) which will help startups to reach Product Market Fit quickly”.

The post Indian Blockchain Incubator Satoshi Studios Opens Applications For 1st Batch appeared first on Analytics India Magazine.

Though the concept of Artificial intelligence has been existing for quite a while, it is only in the recent years that it has picked up on the technology charts and is trending on every industry possible. Becoming one of the best-loved technologies amongst the ingenious minds across the globe, AI demands a blend of computer science, math, cognitive psychology and engineering.

There is no doubt about the fact that soon the demand for professionals trained in Artificial Intelligence would outrun supply. Though there is some overlap of AI with analytics, a proficient AI professional would have deep knowledge on areas like computer vision, NLP, robotics and machine learning.

AI education is still in its early days. Sifting through some of the programs on Artificial Intelligence from around the world, we found that not many educational organizations are offering AI courses yet. Most universities that are offering AI courses are doing so as a specialty of elective within larger computer science masters programs.

Yet some universities, mostly in europe, have a headstart in this area and are providing dedicated marsters programs in AI. With a lot of scope that this area has in store, many universities and institutes across the globe would soon tap onto this opportunity and would start offering training and courses that promises to up the knowledge and skill sets in AI and machine learning.

Analytics India Magazine brings to you the 10 leading centres across the world that offer Masters Program in AI. Please note, these are dedicated master programs in AI. We have not included short term or elective offerings on AI.

KU Leuven – Master of Artificial Intelligence

The Master of Artificial Intelligence programme at KU Leuven explores and builds on these fascinating challenges. For many years, it has provided an internationally acclaimed advanced study programme in artificial intelligence. The multidisciplinary programme trains students from a variety of backgrounds – including engineering, sciences, economics, management, psychology, and linguistics – in all areas of knowledge-based technology, cognitive science, and their applications. The one-year programme, taught entirely in English, is the result of a collaboration between many internationally prominent research units from seven different faculties of the university. It allows you to focus on engineering and computer science, cognitive science, or speech and language technology.

Polytechnic University of Catalonia – Master in Artificial Intelligence

This program is addressed to national and international students who wish to acquire advanced knowledge in AI in order to occupy positions of responsibility in industry, the public sector and academia in Catalonia, Spain or abroad. The program covers many research areas related to the design, analysis and application of AI.

Students who undertake this master’s degree will be equipped to:

• Deal with technically complex problems that require a degree of innovation and/or research.
• Make strategically important decisions within their professional domain
• Pursue doctoral studies within the domain of information and communication technologies at the UPC, the URV, the UB or abroad.

Radboud University – Master’s programme in Artificial Intelligence

• Title/degree:Master of Science (MSc)
• Duration:2 years (120 EC), full time
• Start months: September and February
• Language of instruction: English

The AI Master’s programme at Radboud University has a distinctly cognitive focus. This cognitive focus leads to a highly interdisciplinary programme where students gain skills and knowledge from a number of different areas such as mathematics, computer science, psychology and neuroscience combined with a core foundation of artificial intelligence.

University of Amsterdam – Master’s programme in Artificial Intelligence

• Degree programme: MSc Artificial Intelligence
• Type: Regular study programme
• Mode: Full-time
• Credits: 120 ECTS, 24 months
• Language of instruction: English
• Starts in: September

The Master’s programme in Amsterdam has a technical approach towards AI research. It is a joint programme of the University of Amsterdam and Vrije Universiteit Amsterdam. This collaboration guarantees a wide range of topics, all taught by world renowned researchers who are experts in their field.

University of Edinburgh – MSc in Artificial Intelligence

This MSc is taught at the UK’s longest established centre for artificial intelligence, which remains one of the best in the world.

Our research draws on neuroscience, cognitive science, linguistics, computer science, mathematics, statistics and psychology to span knowledge representation and reasoning, the study of brain processes and artificial learning systems, computer vision, mobile and assembly robotics, music perception and visualisation.

We aim to give you practical knowledge in the design and construction of intelligent systems so you can apply your skills in a variety of career settings.

University of Georgia – MS IN ARTIFICIAL INTELLIGENCE

The Master of Science in Artificial Intelligence (M.S.A.I.) degree program is offered by the interdisciplinary Institute for Artificial Intelligence. Areas of specialization include automated reasoning, cognitive modeling, neural networks, genetic algorithms, expert databases, expert systems, knowledge representation, logic programming, and natural-language processing. Microelectronics and robotics were added in 2000.

University of Groningen – MSc in Artificial Intelligence

• Degree: MSc in Artificial Intelligence
• Course type: Master
• Language of instruction: English (100%)
• Duration: 24 months (120 ECTS)
• Start: February, November, September
• Programme form: full-time
• Faculty: Mathematics and Natural Sciences

The courses taught in the area of cognitive robotics are related to research in social robotics, to the origin of robotic communication and to the way in which robots recognize movement. Research is conducted at the Artificial Intelligence and Cognitive Engineering Institute.

University of Sheffield – MSc Computational Intelligence and Robotics

You will undertake a broad spectrum of modules related to computational intelligence and robotics, including vision, speech, neural networks, mobile robots and autonomous vehicles and computational neuroscience.

You will be taught by world-leading experts from the Departments of Automatic Control & Systems Engineering, Computer Science, and the neuroscience group in Psychology. The teaching will include lectures, seminars, tutorials, individual assignments and a major research project.

This course provides the multi-disciplinary knowledge and skills you will need to meet the demand for experts in computational intelligence.

University of Southampton – MSc Artificial Intelligence

• Degree Awarded: MSc
• Intake: 350 places across all ECS MSc programmes
• Average applications per place: 7

This research-led MSc takes a contemporary approach and covers the fundamental aspects of traditional symbolic and sub-symbolic aspects.

The programme will give you a solid awareness of the key concepts of artificial intelligence. You will also learn the techniques that form the current basis of machine learning and data mining. You will develop a wide-ranging skill set that supports further study or that you can use in application development.

As a result of the leading research being undertaken at Southampton, the course is able to offer a wide range of options that cover state-of-the-art modern techniques, which directly reflect research directions in ECS.

Utrecht University – MSc in Artificial Intelligence

• Title: MSc
• Master’s degree in: Artificial Intelligence
• Programme: Artificial Intelligence
• LANGUAGE OF INSTRUCTION: English
• PART- OR FULL-TIME STATUS: Full-time
• DURATION: 2 years
• CREDITS: 120
• START OF STUDIES: February, September
• FACULTY: Science
• GRADUATE SCHOOL: Natural Sciences

As a graduate of the Artificial Intelligence programme, you will have a solid understanding of the logical, philosophical, and cognitive foundations of AI research. You will also have a good overview of the main AI techniques and an in-depth understanding of how to apply these techniques in at least one of the areas within multi-agent systems, reasoning, or cognitive processing. In addition, you will have the skills to carry out AI research in academic or R&D environments and to identify how AI techniques can provide intelligent solutions to IT problems in companies and organisations.

The post 10 Leading Masters Programs on Artificial Intelligence from around the world appeared first on Analytics India Magazine.

Interested in the lucrative world of credit risk analytics? But lack the domain knowledge and technical capabilities to make the cut?

In a first-of-its-kind course, with its eyes trained on the global market, Genpact, a global leader in digitally powered business process management and transformation and Ivy Professional School have come together once again with an industry intensive, domain-centric program tailored specifically to Financial Risk Analytics – Quantitative Risk Analytics Professional Executive Course (QRA).

Genpact and Ivy Professional School’s association goes back to 2012 when they launched the Risk Academy aimed at tapping freshers and training them to produce a steady stream of talent at entry level. “The program was a tremendous success, and building on the success of the Level 1 program, Genpact and Ivy have decided to launch Level 2 program which QRA is essentially to create an ecosystem of trained resources in risk analytics. QRA is specifically custom made, with credit risk analytics at its core and the major distinguishing factor is the proprietary knowledge and vast experience in BFSI that Genpact will bring to the table,” said Kiran Kumar, Vice President, Service Delivery Lead, Financial Risk Analytics, Genpact India.

What led to the rise of Credit Risk Analytics professionals

The financial market underwent a paradigm shift with the mortgage crisis in 2008-2009 that gave rise to a new set of regulations and multiple guidelines to make the banking industry safer and stronger, Genpact’s Financial Risk Analytics VP Kiran Kumar shared with Analytics India Magazine in an exclusive chat. With that, the banks needed to comply with a new set of guidelines and manage and measure risk in their portfolio better. This in turn spawned the need for Credit Risk Analytics professionals across the geographies with technical expertise such as statistical modelling and domain expertise. There is a need for professionals who have an ability to overlay data analytics and perform statistical modelling, all this with business insights, he noted.

India’s talent gap is heavily underscored by the latest CRISIL report that pegs the current number of professionals in Risk Analytics market in India at s 5,200-5,300. By 2020, the number of people required in risk analytics in India is expected to triple. Overall, the Indian Risk Analytics industry is expected to quadruple to USD 2.5 billion by 2020. Given that India is the preferred destination for business, since most of the companies are serving global clients, analytics accounts for 25% of revenue and IT service segment is another major contributor.

Here are some hard facts about the course:

Type: Classroom and Online (classroom learning takes place in Bangalore over the weekend and students can also learn online)

• Skill Level: Advanced
• Duration: 3 months
• No of seats: 20
• Accreditation: Certificate
• Fee: INR 94,500 + taxes
• Primer Module (no extra cost): 38 hour training on Statistics for risk modeling using SAS & SAS programming (base SAS and Advanced SAS) for students with no prior training in analytics
• Credit Risk Analytics Module: 116 Hours
• Total Duration: 8 to 9 weeks – 8 hours per Saturday and Sunday
• Start Time: January 2017
• How to Apply: Send your CV to info@ivyproschool.com

Course

Co-created by Genpact’s senior Risk Analytics experts and Ivy Pro School faculty members, the three-month program gives an equal thrust to regulatory norms and advanced risk analytics. The course covers the basics of Credit Risk Foundation, fundamentals of Risk Modelling, and a deep dive in Risk Modelling with clustering, decision-trees, advanced modelling techniques — Neural Networks (pros/cons), support vector machines and how they are implemented in risk analytics. There is also an in-depth dive into regulatory topics such as BASEL, CCAR, and DFAST Implementation, ARIMA, credit risk models like loss forecasting, PD, LGD and EAD, banking regulations.

The academic lead from the Ivy Pro School faculty team for this course brings solid 10+ year experience in Risk analytics with American Express (AMEX) and other risk analytics firms. She has a strong base in developing strategies using quantitative statistical tools (SAS) and models and setting up fraud detection frameworks to identify and mitigate losses. Students will also benefit from real, industry specific guest lectures imparted by Genpact’s senior management.

Industry intensive experience from Live Case Studies

One of the major differentiator for the program is the live case studies that would be conducted using the techniques and methodologies that are currently being used in the Risk Analytics industry. And Genpact’s senior Risk Analytics experts from the global risk analytics team would share their industry experiences through these case studies. “There will be handpicked live case studies and we will be replicating the techniques and methodology applied in real life scenarios. The resources will be trained in how to enrich data, validate the models and how to tap it up with regulatory guidelines and use individual judgment in business,” said Kiran Kumar.

1. Survival Analysis
2. Penalized Models
3. Hazard Models
4. ARIMA
5. Credit Score Verification Frameworks
6. Rare Event Modeling
7. Point-in-time and Through-the-cycle Probability of Default (PD)
8. How Exposure at Default (EAD) is calculated for Loan Products vs Products with Limits
9. Economic Loss Given Default (LGD)
10. BASEL, CCAR, and DFAST Implementation

Placement Assistance

Career assistance and placement is a key factor in every program. As the course is yet to roll out in January so we are unable to comment on the placement aspect. However, Genpact revealed that though they are not “committed to any number”, the company will absorb employees on a need-basis. “The placement aspect is mutual, the resources can apply to other companies also but Genpact would definitely give preference to talent generated from L2 program,” shared Kiran Kumar.

Ivy Pro School’s alumni are working across illustrious companies such as KPMG, Deloitte, Wipro, McKinsey, E&Y and Capgemini among others who they will be tapping into for placement assistance.

Why QRA is the right springboard for kick-starting a career in credit risk analytics

We believe Genpact’s partnership with Ivy Professional School as part of a Memorandum of Understanding (MoU) brings a lot of heft to the course. Genpact’s position as a market leader in delivering integrated Credit Risk Solutions, its proprietary knowledge and top-notch technical capabilities deliver great value to the program and is the right platform for creating an “eco-system of trained resources in credit risk analytics industry”. Remember, Genpact’s biggest market is BFSI and as reports indicate, global risk analytics market is billed to reach USD 50-51.0 billion by 2020.

As Prateek Agrawal, Director of Ivy Pro School said, “Genpact adds great value addition in terms of content, the curriculum is co-created by Genpact, students can benefit from guest lectures, live case studies where they will work in real case scenarios with anonymized data. Students will be mentored by industry veterans and to top it all we are offering classroom experience which no one else is offering.”

Here are our Pros:

• Classroom learning (anchored in Bangalore) – makes for an intensive and interactive learning experience
• Classes are available online also
• Genpact’s value addition are manifold — in curriculum, guest lectures, live case studies, mentoring and placement
• Excellent faculty members from Ivy Pro School
• Immersive, industry intensive course

Cons

Since the batch is yet to roll out, there is no precedent for placement

Overall, if you are somewhat in quantitative area and want to build up your knowledge in credit risk analytics and risk management and gain a learning experience, then this certification is right up your alley.

The post Review: Quantitative Risk Analytics Executive Course by Ivy Pro School & Genpact appeared first on Analytics India Magazine.

Artificial Intelligence has always had a huge dedication in Hollywood Sci-fi releases since as early as 2001: A Space Odyssey (1968). Here we explore 10 upcoming movies this year that have components of intelligent robots and artificial intelligence.

1. Alien Covenant

Bound for a remote planet on the far side of the galaxy, members (Katherine Waterston, Billy Crudup) of the colony ship Covenant discover what they think to be an uncharted paradise. While there, they meet David (Michael Fassbender), the synthetic survivor of the doomed Prometheus expedition. The mysterious world soon turns dark and dangerous when a hostile alien life-form forces the crew into a deadly fight for survival.

Initial release: 19 May 2017 (USA)

Director: Ridley Scott

Film series: Alien

2. Annihilation

Two years on from their storming Ex Machina, Alex Garland and Oscar Isaac are back with the story of a biologist (Natalie Portman) who signs up for “a dangerous, secret expedition where the laws of nature don’t apply”. Jennifer Jason Leigh is a – we’re assuming – slightly unhelpful psychologist.

3. Anon

Anon is an upcoming science fiction thriller film directed and written by Andrew Niccol. The film stars Clive Owen and Amanda Seyfried. Set in a Soviet-style future where the government is trying to fight crime by eliminating privacy, that means, anything you do or say is totally illegal, thus creating total censorship. A police officer named, Sal Frieland (Clive Owen), is a tough cop who is doing his best to fight crime in his own city.

Director: Andrew Niccol

Written by: Andrew Niccol

4. Blade Runner 2049

Officer K (Ryan Gosling), a new blade runner for the Los Angeles Police Department, unearths a long-buried secret that has the potential to plunge what’s left of society into chaos. His discovery leads him on a quest to find Rick Deckard (Harrison Ford), a former blade runner who’s been missing for 30 years.

Initial release: 6 October 2017 (USA)

Director: Denis Villeneuve

Film series: Blade Runner Film Series

5. Future World

The movie is set in the barren landscape of a post-apocalyptic world, where a young Prince from the Oasis (one of the last known safe-havens) and a robot named Ash go on a daring journey of self-discovery – one that winds through the violent and desolate world of the Wastelands.

6. Ghost in the Shell

The Major (Scarlett Johansson), a special ops, one-of-a-kind human-cyborg hybrid, leads an elite task force known as Section 9. Devoted to stopping the most dangerous criminals and extremists, Section 9 is faced with an enemy whose singular goal is to wipe out Hanka Robotic’s advancements in cyber technology.

Initial release: 29 March 2017 (France)

Director: Rupert Sanders

7. Inherit the Earth

The last survivor on earth, a young girl, is protected by a group of robots from a pack of zombies that are intelligent and evolved.

Still in production and release date to be announced.

8. Mute

Mute is an upcoming British science fiction mystery film directed by Duncan Jones, who also co-wrote the script with Michael Robert Johnson and Damon Peoples.

Initial release: 2017 (USA)

Director: Duncan Jones

Film series: Moon

9. Resident Evil: The Final Chapter

Alice continues her battle against The Umbrella Corporation’s AI system The Red Queen with help from a group of bad-asses including her daughter, Becky. Their epic battle will lead them back to where it all began in the dark mansion built by Umbrella founder Lord Ozwell E. Spencer and the deep underground research and development center known as The Hive, where The Red Queen plots total destruction of the human race.

10. Transformers: The Last Knight

Having left Earth in search of the Creators, Optimus Prime returns. Two worlds collide but only one will survive.

Initial release: 23 June 2017 (USA)

Director: Michael Bay

The post 10 Movies to look forward to in 2017 on Artificial Intelligence appeared first on Analytics India Magazine.

An analytical model is a statistical model that is designed to perform a specific task or to predict the probability of a specific event.

In layman terms, a model is simply a mathematical representation of a business problem. A simple equation y=a+bx can be termed as a model with a set of predefined data input and desired output. Yet, as the business problems evolve, the models grow in complexity as well. Modeling is the most complex part in the lifecycle of successful analytics implementation.

Scalable and efficient modeling is critically consequential to enable organizations to apply these techniques to ever-more sizably voluminous data sets for reducing the time taken to perform these analyses. Thus models are engendered that implement key algorithms to determine the solution to our business quandary.

Supervised vs Unsupervised learning models

Supervised Learning models are the models where there is a clear distinction between explanatory and dependent variables. The models are trained to explain dependent variables using explanatory variables. In other words, the model output attributes are known beforehand. Eg:

• Prediction (e.g., linear regression)
• Classification (e.g., decision trees, k‐nearest neighbors)
• Time‐series forecasting (e.g., regression‐based)

In unsupervised learning, the model outputs are unknown or there are no target attributes: there is no distinction between explanatory and dependent variables. The models are created to find out the intrinsic structure of data. Eg:

• Association rules
• Cluster analysis

Here we plan to briefly discuss the following 10 basic algorithms/ techniques that any data scientist should have in his/her arsenal. There are many more techniques that are powerful, like Discriminant analysis, Factor analysis etc but we wanted to focus on these 10 most basic and important techniques.

1. Hypothesis Testing

2. Linear Regression

3. Logistic Regression

4. Clustering

5. ANOVA

6. Principal Component Analysis

7. Conjoint Analysis

8. Neural Networks

9. Decision Trees

10. Ensemble Methods

1. Hypothesis Testing

Hypothesis testing is not exactly an algorithm, but it’s a must know for any data scientist. Do not move ahead before you completely master this technique.

Hypothesis testing is the process in which statistical tests are used to check if a hypothesis is true or not using the data. Based on hypothetical testing, we choose to accept or reject the hypothesis. When an event occurs, it can be a trend or happens by chance. To check whether the event is an important occurrence or just by chance, hypothesis testing is necessary.

There are many tests for hypothesis testing, but the following 2 are most popular:

1. t-test: t-test is a popular statistical test to make inferences about single means or inferences about two means or variances to check if the two groups’ means are statistically different from each other where n<30 and standard deviation is unknown.
2. Chi-square test: A chi square (χ2) test is used to examine if 2 distributions of categorical variables are significantly different from other.

2. Linear Regression

Linear regression is a statistical modelling technique, which attempts to model the relationship between an explanatory variable and a dependent variable by fitting the observed data points on a linear equation. For eg: Modelling the BMI of individuals using weight.

A linear regression is used if there is relationship or significant association between the variables. This can be checked by scatterplots. If no association appears between the variables, fitting a linear regression model to the data will not provide useful model.

A linear regression line has equation in the following form:

Y = a + bX,

Where, X = explanatory variable and

Y = dependent variable.

b = slope of the line

a = intercept (the value of y when x = 0).

3. Logistic Regression

Logistic regression is the technique to find relationship between a set of input variables and a output variable (just like any regression) but the output variable in this case would be a binary outcome (think of 0/1 or yes/no).

For eg: Will there be traffic jam in a certain location in Bangalore is a binary variable. The output is a categorical Yes or no.

The probability of occurrence of traffic jam can be dependent on attributes like weather condition, day of week and month, time of day, number of vehicles etc. Using logistic regression, we can find the best fitting model that explains the relationship between independent attributes and traffic jam occurrence rates and predicts probability of jam occurrence.

4. Clustering Techniques

Clustering (or segmentation) is a kind of unsupervised learning algorithm where a dataset is grouped into unique, differentiated clusters.

Lets say, we have customer data spanning 1000 rows. Using clustering we can group the customers into differentiated clusters or segments, based on the variables. In case of customers’ data, the variables can be demographic information or purchasing behavior.

Clustering is an unsupervised learning algorithm because the output is unknown to the analyst. We do not train the algorithm on any past input – output information, but let the algorithm define the output for us. Therefore (just like any other modeling exercise), there is no right solution to clustering algorithm; rather the best solution is based on business usability. Some people also call Clustering as unsupervised classification.

There are 2 basic types of clustering techniques:

• Hierarchical clustering
• Partitional clustering

5. ANOVA

The one-way analysis of variance (ANOVA) test is used to determine whether the mean of more than 2 groups of dataset are significantly different from each other.

For eg. A campaign of BOGO (Buy one get one) is executed on 5 groups of 100 customers each. Each group is different in terms of their demographic attributes. We would like to determine whether these 5 respond differently for the campaign. This would help us optimize the right campaign for the right demographic group, increase the response rate and reduce cost of campaign.

The “analysis of variance” works by comparing the variance between the groups to that of within group variance. The core of this technique lies in the assessing whether all the groups are infact part of one larger population or completely different population with different characteristics.

6. Principal Component Analysis

Dimension (variable) reduction techniques aim to reduce the data set with higher dimension to that of lower dimension without the loss of feature of information that is conveyed by the dataset. The dimension here can be conceived as the number of variables that a data set contain.

Two commonly used variable reduction techniques are:

1. Principal Component Analysis (PCA)
2. Factor Analysis

The crux of PCA lies in measuring the data from perspective of a principal component. A principal component of a data set is the direction with largest variance. A PCA analysis involves rotating the axis of each variable to highest Eigen vector/ Eigen value pair and defining the principal components i.e. the highest variance axis or in other words the direction that most defines the data. Principal components are uncorrelated and orthogonal.

7. Conjoint Analysis

Conjoint analysis is widely used in market research to identify customers’ preference for various attributes that make up a product. The attributes can be various features like size, color, usability, price etc.

Using conjoint (tradeoff) analysis, brand managers can identify which features would customer’s tradeoff for a certain price points. Thus it is highly used technique in new product design or pricing strategies.

8. Neural Networks

Neural network (also known as Artificial Neural Network) is inspired by human nervous system, how complex information is absorbed and processed by the system. Just like humans, neural networks learn by example and are configured to a specific application.

Neural networks are used to find patterns in complex data and thus provide forecast and classify data points. Neural networks are normally organized in layers. Layers are made up of a number of interconnected ‘nodes’. Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done. The hidden layers then link to an ‘output layer’ where the answer is output as shown in the graphic below.

9. Decision Trees

Decision trees, as the name suggest, is a tree-shaped visual representation of one can reach to a particular decision by laying down all options and their probability of occurrence. Decision trees are extremely easy to understand and interpret. At each node of the tree, one can interpret what would be the consequence of selecting that node or option.

10. Ensemble Methods

Ensemble methods works on the philosophy that many weak learners can come together to give a strong prediction. Random forest is currently the most accurate of all classification techniques available. Random forest is an ensemble method. In this case, the weak learner is a simple decision tree and random forest is strong learner.

Random forest optimizes the output from many decision trees formed from sample of same dataset. Thus finding the most accurate of classification model.

The post 10 Machine Learning Algorithms every Data Scientist should know appeared first on Analytics India Magazine.

Over years, Hadoop has become synonymous to Big Data. Talk about big data in any conversation and Hadoop is sure to pop-up. But like any evolving technology, Big Data encompasses a wide variety of enablers, Hadoop being just one of those, though the most popular one.

Here we list down 10 alternatives to Hadoop that have evolved as a formidable competitor in Big Data space.

Also read, 10 Most sought after Big Data Platforms

1.  Apache Spark 

Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley’s AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.

Read Hadoop vs Spark: Which is the best data analytics engine?

2.   Apache Storm

Apache Storm is a distributed stream processing computation framework written predominantly in the Clojure programming language. Originally created by Nathan Marz and team at BackType, the project was open sourced after being acquired by Twitter. It uses custom created “spouts” and “bolts” to define information sources and manipulations to allow batch, distributed processing of streaming data. The initial release was on 17 September 2011.

A Storm application is designed as a “topology” in the shape of a directed acyclic graph (DAG) with spouts and bolts acting as the graph vertices. Edges on the graph are named streams and direct data from one node to another. Together, the topology acts as a data transformation pipeline. At a superficial level the general topology structure is similar to a MapReduce job, with the main difference being that data is processed in real time as opposed to in individual batches. Additionally, Storm topologies run indefinitely until killed, while a MapReduce job DAG must eventually end.

3.   Ceph

Ceph, a free-software storage platform, implements object storage on a single distributed computer cluster, and provides interfaces for object-, block- and file-level storage. Ceph aims primarily for completely distributed operation without a single point of failure, scalable to the exabyte level, and freely available.

Ceph replicates data and makes it fault-tolerant, using commodity hardware and requiring no specific hardware support. As a result of its design, the system is both self-healing and self-managing, aiming to minimize administration time and other costs.

On April 21, 2016, the Ceph development team released “Jewel”, the first Ceph release in which CephFS is considered stable. The CephFS repair and disaster recovery tools are feature-complete (snapshots, multiple active metadata servers and some other functionality is disabled by default).

4. DataTorrent RTS

DataTorrent RTS is an enterprise product built around Apache Apex, a Hadoop-native unified stream and batch processing platform. DataTorrent RTS combines Apache Apex engine with a set of enterprise-grade management, monitoring, development, and visualization tools.

DataTorrent RTS platform enables creation and management of real-time big data applications in a way that is

• highly scalable and performant – millions of events per second per node with linear scalability
• fault tolerant – automatic recovery with no data or state loss
• Hadoop native – installs in seconds and works with all existing Hadoop distributions
• easily developed – write and re-use generic Java code
• easily integrated – customizable connectors to file, database, and messaging systems
• easily operable – full suite of management, monitoring, development, and visualization tools

5. Disco

Disco is a lightweight, open-source framework for distributed computing based on the MapReduce paradigm.

Disco is powerful and easy to use, thanks to Python. Disco distributes and replicates your data, and schedules your jobs efficiently. Disco even includes the tools you need to index billions of data points and query them in real-time.

Disco was born in Nokia Research Center in 2008 to solve real challenges in handling massive amounts of data. Disco has been actively developed since then by Nokia and many other companies who use it for a variety of purposes, such as log analysis, probabilistic modelling, data mining, and full-text indexing.

6. Google BigQuery

BigQuery is Google’s fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don’t need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.

7.  High-Performance Computing Cluster (HPCC)

HPCC (High-Performance Computing Cluster), also known as DAS (Data Analytics Supercomputer), is an open source, data-intensive computing system platform developed by LexisNexis Risk Solutions. The HPCC platform incorporates a software architecture implemented on commodity computing clusters to provide high-performance, data-parallel processing for applications utilizing big data. The HPCC platform includes system configurations to support both parallel batch data processing (Thor) and high-performance online query applications using indexed data files (Roxie). The HPCC platform also includes a data-centric declarative programming language for parallel data processing called ECL.

8. Hydra 

Hydra is a distributed data processing and storage system which ingests streams of data (think log files) and builds trees that are aggregates, summaries, or transformations of the data. These trees can be used by humans to explore (tiny queries), as part of a machine learning pipeline (big queries), or to support live consoles on websites (lots of queries).

You can run hydra from the command line to slice and dice that Apache access log you have sitting around (or that gargantuan csv file). Or if terabytes per day is your cup of tea run a Hydra Cluster that supports your job with resource sharing, job management, distributed backups, data partitioning, and efficient bulk file transfer.

9.  Pachyderm

Pachyderm is a data lake that offers complete version control for data and leverages the container ecosystem to provide reproducible data processing. Data and Code were meant to be unified. Containerizing them together unlocks Reproducibility and Collaboration for your team.

Running your code in a container and accessing the data through Pachyderm’s version control system (PFS) guarantees that the analysis is Reproducible. And because it’s just a container, you can use any language or libraries you want.

Reproducibility is the requirement for true Collaboration. By enabling Reproducibility with containers, Pachyderm allows each team member to develop data analysis locally and then seamlessly push the same code into a production cluster.

10. Presto

Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.

Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook.

Presto allows querying data where it lives, including Hive, Cassandra, relational databases or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization.

Presto is targeted at analysts who expect response times ranging from sub-second to minutes. Presto breaks the false choice between having fast analytics using an expensive commercial solution or using a slow “free” solution that requires excessive hardware.

The post 10 Hadoop Alternatives that you should consider for Big Data appeared first on Analytics India Magazine.

The progression of prescriptive analytics has advanced to data sciences, by now. Exponential focus around digital transformation paired with emergence of plug and play algorithms is becoming crucial for survival of business enterprises. Being able to tap into this dark side of data and act on the insights, is a key differentiator between digital attackers and the laggards. The key to the future in leveraging accurate algorithms, lies in the ability to harness its use in addressing complex business problems.

The third dimension (apart from proliferation of data and computing power), is the quality of the algorithms which is used to process this massive data and derive meaning to drive decisions. The use of complexity-driven algorithms and machine learning techniques allow firms to go beyond market segmentation, for instance, the ability to develop the driverless car, or the capacity to identify cancerous cells in images, all require the combination of deep learning algorithms applied to large datasets.

In the last 3 years, advancement is algorithms have gone beyond imitating easily replicable skills, and now foraying increasingly into  areas that calls for a higher level of expertise – such as driving cars, diagnosing medical images, playing chess or Go, and even engaging in creative tasks such as poetry or painting. Widely considered to be the next frontier in technology, the best-in-breed companies are investing on robust algorithms in various functions of business. What these algorithms are capable of in future, is only limited by human imagination.

Mankind is faced with humongous challenges in areas as diverse as education, healthcare, infrastructure and financial inclusion. Can we expand the scope of Algorithms and apply it to a broader range of problem solving to not just automate business, but make human existence more comfortable and meaningful as well?

With these facets in mind, the 3^rd Business Analytics Conclave to be held on 24^th February, 2017 has crafted the theme, “RISE OF ALGORITHM ECONOMY: ANALYTICS FOR COMPETITIVE ADVANTAGE”. The conclave will focus on how organizations can marry best of the breed algorithms to the realities of the complex marketplace and how algorithms are becoming fulcrum to the digital transformation for the enterprises.  

Listen to the best minds from the Data Analytics industry share their experiences, thoughts and experiences and find out more about the many opportunities that Data Analytics has to offer. These include:

Title: How Advancements in Algorithms are Driving Innovation and Transformation within Businesses 

Christopher Arnold, Knowledge Service Leader, Wells Fargo & Company

Sanjay Srivastava, Director, Analytics, A Fortune 500 Company

Title: Being Digital – How AI, IoT, RPA, Automation are Redefining the New Technology Landscape 

Pankaj Rai, Sr. Vice President (Strategy), Wells Fargo & Company

Title: Talent Sciences: Revolutionizing the entire facets of HR

Kenneth Wheeler, Asst. Vice President, HR, LogiNext Solutions

Attendance is by invitation only. Limited places available. For registration, please email at dsmith@upes.ac.in or call Deborah Smith on 0-9910115658 or 011 41730151/53 (Extn. 3171).

The post 3rd Business Analytics Conclave on Rise of Algorithm Economy: Analytics for Competitive Advantage: February 24, 2017, Bengaluru appeared first on Analytics India Magazine.

Investors pumped in $213 million across over 20 deals in Indian startups in 2016. Though the investment appears to dwarf the total investments in all startups in India, analytics still forms a significant rising technological domain which is eminent from the big PE names below.

Read Top 10 Investors in Indian Analytics firms – 2015

1. Khazanah Nasional Berhad

Investment: Fractal Analytics

Khazanah Nasional Berhad is the sovereign wealth fund of the Government of Malaysia.

Khazanah holds and manages selected commercial assets of the Government and undertakes strategic investments on behalf of the nation. It is involved in sectors such as power, telecommunications, finance, healthcare, aviation, infrastructure, leisure and tourism, and property, amongst others. The fund is a member of the International Forum of Sovereign Wealth Funds,[1] which maintains and promotes the Santiago Principles on best practices in managing sovereign wealth funds.

Its portfolio includes Axiata, CIMB, Tenaga Nasional, IHH Healthcare, UEM Group, Telekom Malaysia, Malaysia Airlines, and Malaysia Airports.

2. Lightspeed Venture Partners

Investment: Qubole & Innovacer

Lightspeed Venture Partners is a venture capital firm focusing on early and expansion stage investments in the consumer, enterprise technology and cleantech markets.

Lightspeed Venture Partners has backed more than 200 companies, including Brocade (BRCD), DoubleClick (acquired by Google after going public), Nicira (acquired by VMware), Playdom (acquired, DIS), Pliant Technology (acquired, SanDisk), XtremeIO (acquired, EMC), Blue Nile (NILE), Fusion-io (FIO), Phone.com (OPWV), Informatica (INFA), and Solazyme (SZYM). The fund has 24 early stage enterprise investments that have gone public, the most of any fund in the world.

3. Access Asset Managers

Investment: Beroe

Access Asset Managers is a fund manager set up to focus on Private Equity Investments in the Indian SME sector. The Manager is in the process of launching Access India Fund – I invest in high growth small and mid-cap companies in India

The two principals of the Investment Team, Nilesh Mehta and Sangeeta Modi are seasoned investment professionals. They have collectively over forty five years of investment experience in Indian debt and equity of which twenty years are in the private equity industry.

Access believes that the best results can be achieved when the fund manager can partner with the management of the investee company to assist in growth. Typically Access attempts to provide guidance on both operational and strategic issues.

To this extent, Access’ management team also includes prominent entrepreneurs with real and relevant experience of managing businesses in addition to the Investment Team.

4. Edelweiss Private Equity

Investment: Bridgei2i

Edelweiss Private Equity (PE) is the venture capital and private equity arm of Mumbai-based financial services firm Edelweiss Financial Services Ltd. It is focused on future demand areas including wearable technologies, internet of things, data analytics, fintech, health and hygiene. The fund was set up in May 2015.

5.  Charles River Ventures

Investment: Qubole

CRV (a.k.a. Charles River Ventures) is a venture capital firm focused on early-stage investments in technology and new media companies. The firm, which is based in Cambridge, Massachusetts and Menlo Park, California, was founded in 1970 to commercialise research that came out of MIT. (Its name comes from the Charles River that divides Boston and Cambridge)

The firm has raised over $2.1 billion since inception across 16 funds. Upon closing of the 16th fund, the firm rebranded to CRV. Prior to that, the firm’s most recent fund, CRV XV, closed in February 2012 with $375 million of investor commitments. CRV’s 14th fund raised $320 million of commitments.

6. Institutional Venture Partners

Investment: Qubole

Institutional Venture Partners (IVP) is a US-based private equity investment firm focusing on later-stage venture capital and growth equity investments. IVP is one of the oldest venture capital firms on Sand Hill Road founded in 1980.

7. Norwest Venture Partners

Investment: Qubole

Norwest Venture Partners (Norwest) is a venture and growth equity investment firm with approximately $6B in capital under management.

The firm targets early- to late-stage venture and growth equity investments across several sectors, including cloud computing and information technology, Internet and consumer, software as a service, business and financial services, and healthcare.

8. Pravega Ventures

Investment: Innovacer

Pravega Ventures, founded in 2016, is an early stage Venture Capital fund focused on providing seed and pre-Series A funding to companies.

9. WestBridge Capital

Investment: Innovacer

WestBridge Capital is a highly experienced investment firm, managing over $2 billion of capital, which focuses on investments in India. WestBridge seeks to partner with some of India’s most promising mid-sized companies run by outstanding entrepreneurs and management teams for the long-term, whether they are public or private. A typical investment ranges from $10 million to $80 million, often resulting in a substantial minority equity ownership, second only to the founder in many cases.

WestBridge was co-founded by KP Balaraj, Sumir Chadha, SK Jain and Sandeep Singhal. The same team of four also co-founded Sequoia Capital, India. In the last fifteen years, the team has led investments in over 80 companies and oversaw a total investment of over $1.6 billion. The six funds that invested on the advice of the team, have raised over $3.2 billion in capital. The team is one of the most recognized in the industry, and has a combined 50+ years of experience in investing in Indian companies.

10. Sequoia India

Investment: Vymo

Sequoia Capital is an American venture capital firm. The firm is located in Menlo Park, California and mainly focuses on the technology industry. It has backed companies that now control $1.4 trillion of the combined stock market value. Sequoia manages multiple investment funds including funds specific to India, Israel, and China.

The post The top 10 Investors of Indian Analytics startups in 2016 appeared first on Analytics India Magazine.