Business Analytics Area


We live in a data-driven economy. Data is now being created at rates faster than ever before, in many different forms such as text, audio and video. In 2019 alone, 5 zetabytes of data were created and is expected to be 175 zetabytes by 2025(E) as per IDC and Forbes. Because of the proliferation of data and data analytic tools, McKinsey and Harvard have forecast that the role of a data scientist will be among the top careers in the 21st century.

At IFIM Business School, we firmly believe in the central role that data analytics plays in today’s decision-making processes, and have developed the vision to become India’s leading center for Business Analytics in the next 3-5 years. In line with that vision, we have curated the curriculum to bring state-of-the-art technology to our students. Because data analytics from the perspective of a business school implies applications in Marketing, Finance and Supply Chain, our faculty members offer courses such as Marketing Analytics, BlockChain, and Supply Chain and Logistics Analytics, to name a few. These courses are taught by experienced faculty members with strong academic credentials and rich industry experience. Some of these courses are taught directly by industry practitioners who are experts in their respective subjects.

Highlights of the Area

Analytics area interfaces with other leading centers such as McCombs School of Business at University of Texas Austin for refining its content and for masterclasses by academic experts.

We have a strong association with the National Association for Software and Services Companies (Nasscom), in particular with its AI Cell, to promote teaching of AI in our curriculum and for understanding the industry requirements in the field of AI.

As part of our institutional collaboration with leading analytics company INSOFE, we run our superspecialisation in Analytics.

A number of prominent industry speakers from top companies such as IBM, Deloitte and others have made guest lectures to our students. These include topics such as Big Data Analytics, Convolutional Neural Networks, etc.

In addition to coursework at IFIM, students of Analytics pursue certifications from reputed organizations such as Coursera, Udemy, Python Institute, Microsoft and others. In fact, going forward the completion of certificate from identified organizations will be rewarded in the form of a grade point increase in certain courses at IFIM. Students also participate in Analytics competitions at Kaggle, which help them to hone their knowledge further and foster a sense of teamwork for problem solving.


List of Courses Offered

At IFIM, we believe in practice-driven, hands-on approach towards teaching. This philosophy is adopted in Analytics teaching in a major way. Every single course that we teach is a combination of the relevant concepts and theory, and hands-on practice using software such as Python, R, Excel and others.

a. Core Courses - Quantitative Methods, Spreadsheet Modeling, Decision Making Science and Proficiency in Business Tools

b. Elective Courses - Business Data Visualization, Coding Business Applications with R and Python, Data Management systems and Data Engineering, Predictive Analytics in Business with R and Python, Big Data Analytics, Cloud Computing for Business Value, Information Security and Cyber Law, Text Mining and Sentiment Analysis, Block chains and business applications, Business Forecasting, Supply Chain & Logistics Analytics, Advanced Predictive Analytics, Artificial Intelligence and Machine Learning, Marketing Analytics, Computer Vision & Image Analytics Managerial Applications, Advanced Risk Analytics, and Financial Technologies (FinTech).

c. Super Specialization Courses - Analytics area offers a superspecialisation in collaboration with INSOFE, the leading data analytics training and consulting firm. The superspecialisation is run as a full term (Term V), after they have taken the elective courses, and takes a fully problem-solving approach to learning. Initially, the students are introduced to topics which are relatively advanced for them, specifically Convolutional Neural Networks and Recurrent Neural Networks. Experienced data scientists from INSOFE engage with the students in a week-long lecture-cum-lab mode to discuss these topics and solve related problems. In the second part of the superspecilisation, students are given an industry problem to solve using one of the techniques learned in the course. They do this using Python in Google Colab. Successfully completion of the superspecialisation ensures that students are conceptually strong and totally hands-on and ready to launch their career in the field of analytics.

Title Description Domain
(A) Predicting the alpha signal using microblogging data A hedge fund uses 6 financial factors to predict the alpha signal in a stock. This alpha signal is used to make purchase decisions about the stock. The hedge fund now collected and tagged microblogging data for sentiment from the Social Media platform called ‘StockTwits’.
StockTwits is used by people who regularly trade stocks. People on this platform tweet about stocks using the special character ‘$’ to indicate the name of the stock. These microblogs similar to tweets might contain important information about the alpha signal in a stock.
Your goal is to build a sentiment analysis model using the tagged data. The problem statement is to build a sentiment analysis model that should then be used to generate a new stock factor which together with the other stock factors should be used to predict the Alpha Signal.
(B) Predicting the Women's clothing rating based on the customer reviews. This is a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. Its nine supportive features offer a great environment to parse out the text through its multiple dimensions. Because this is real commercial data, it has been anonymized, and references to the company in the review text and body have been replaced with “retailer”. Retail
(C) Predicting the author of a Speech based on the script One of the things the Presidential Election Campaign of 2011/2016 may be remembered for is the proliferation of fake news stories.Misinformation is false or inaccurate information that is mistakenly or inadvertently created or spread; the intent is not to deceive. With the scale of news articles generated everyday, identifying the source is becoming an increasingly complicated problem. Hence, there is a need for using predictive modelling as a catalyst for tackling this problem
In this problem statement , we take the first step in solving this by identifying the source of the speech by just looking at the speech
(D) Building a Fashion(Garment) recommendation system based on Images of past orders and reviews E-commerce has been gathering a lot of user base in the past few years. With so many varieties
of products, different categories of products, user preference, recommending a product is very much in interest for any organization.
In this problem statement we will try to recommend retail garment/accessories based upon same type of garment/accessory, price, and it's previous reviews.
(E) Food classification for Grocery store management A grocery store wants to automate figuring out the item vacancies in the racks and then immediately reporting them to the respective departments requesting a rack-refill. It achieves it by identifying the vacancies first, and then figuring out to which food class that particular part of the rack belongs to.
As part of the project the company wants you to classify various images into different classes of food. Given to you is a dataset consisting of 5,000 images covering 25 different classes of groceries, with at least 97 images per class.

Roles that Students are prepared for

In the past, Analytics students have got placed in associate/analyst roles in well-known organizations such as Nuance, Hexaware Technologies, Enquero Global, Mu Sigma, HGS Global Solutions, Optimal Strategix Group (OSG), Nopaperforms, and others in their Analytics & Data Science teams. Students graduating from the E-PGDM (Business Analytics) and PGDM-WP programmes are working in reputed organizations such as NASSCOM, IBM, Shell, Deloitte, Cognizant and others in mid-to-senior managerial roles.

With the collaboration with INSOFE and the start of superspecialisation, students are now being prepared for higher positions in the Analytics field. These include roles such as Data Scientist/Junior Data Scientist, Big Data Analyst, Data Engineer, Supply Chain Analyst and others.


Key Faculty

The Analytics Area at IFIM prides in its rich faculty strength with outstanding mix of academic accomplishments and industry experience. Many of them have played CXO roles in industry. Distinguished international faculty from leading global B Schools like Darden School of Business; McCombs Austin; State University of New York - Albany; Asian Institute of Management, Manila makes the academic experience at IFIM one of the best in the country.

Many international scholars in residence and young bright faculty at IFIM will help you with supervised learning and carrying out academic research and project to ensure you enjoy your learning at IFIM Business School.

Some of the Key Faculty & their respective Courses are listed below:

S.No. Course Key Faculty
1 Business Data Visualization Dr. Kalyan Sengupta
2 Artificial Intelligence and Machine Learning Dr. Supriyo Ghose
3 Text Mining and Sentiment Analysis Dr. Chandrasekhar S
4 Predictive Analytics in Business with R and Python Dr. Parvathy Jayaprakash
5 Decision Making Science Dr. Ellur Anand and Ms. Jamuna Vignesh
6 Block Chain Prof. Soumya Choudhury
7 Quantitative Techniques in Management Dr. Bhavya Tripathi

Area faculty members

Dr. Chandrashekar Subramanyam

Senior Professor

Dr. Kalyan Sankar Sengupta


Dr. Supriyo Ghose

Professor & Area Chair

Mr. Soumya Choudhury

Associate Professor and Programme Chair, PGDM DS

Dr. Ellur Anand

Assistant Professor

Dr. Bhavya Tripathi

Assistant Professor

Dr. Parvathi Jayaprakash

Assistant Professor

Ms. Jamuna Vignesh

Research Associate

Area faculty members are eminent scholars in their own fields. They have presented papers in top international conferences on Artificial Intelligence, Machine Learning, Predictive Analytics, and Text Analytics and Sentiment Analysis. These conferences include Association for the Advancement of Artificial Intelligence (AAAI), NASSCOM Big data Conference at Hyderabad, IEEE Conference at Vellore Institute of Technology, Workshop on Information Systems and Technologies at Charlotte, USA, and many more. They have also conducted a number of Management Development Programs in Artificial Intelligence and Machine Learning.

Prof. Chandrasekhar S has filed a patent in the area of Credit Rating Transition early warning system using Financial and sentiment extracted from various company news sources. He is also nominated member of IIT Kanpur Alumni Association Bangalore chapter in Innovation & Entrepreneur working group.

Professors from the Area have guided a number of students in Doctoral Work.

Faculty members in Analytics have collaborated with senior industry leaders in an “Analytics Think Tank” to understand industry 4.0 requirements from MBA graduates.