In the past two
decades, drastic global changes have been experienced in every society due to rapid and
disruptive advancements in the field of IT and related areas. The growth of computational power,
abundance of data-generation sources such as social media, smartphones and sensors, availability
of cloud and artificial intelligence technologies, along with the prevalence of Big Data have
enabled successful digital transformation in all corners of business and technology. In fact,
the power of analytics on data been felt so greatly that industries now believe “Data is the New
Oil”.
As a result, the required skill bases for the new normal activities have shifted dramatically,
generating a hunger for new skills in various industries.
Amid such a skill crisis, JAGSoM has come up with a technology-driven and practice-oriented
Business Analytics Career Track in the PGDM program. Aspiring graduates are provided with a
ten-credit course offered by business houses and academics. The course will have the primary
objective of keeping pace with technological and managerial advancements using AI and machine
learning.
This Career Track course includes several advanced data-oriented skill sets which are taught
end-to-end. Contextual data analyses including pre-processing, visualization, machine learning
models, deep learning models, and optimization algorithms are the central focus. A Capstone
project – Request For Problem (RFP) project is also included; where student consulting teams
help to solve real-life business problems. Each team is advised by a full-time faculty member
(or an interdisciplinary team of faculty members).
The Career Track in Analytics brings to the students a deep experience in analytics
model-building, validation, and testing. Participants will build on their existing knowledge in
classification methods such as Decision Trees, Neural Networks, Bayesian Classification and will
get a thorough, hands-on experience in analytical topics such as Imbalanced Classification, ROC
Curves, Bagging and Boosting, Ensemble Methods, Applications such as Forecasting, Market Basket
Analysis, Financial Models etc. Participants will also get to use state-of-the-art technologies
such as Keras, Tensorflow. Google Colab, etc. for developing and running the various algorithms.
A special feature of this Career Track will be a Workshop on User Generated Content Analytics
(UGCA) by a renowned Analytics faculty from University of Texas at Austin.
On the successful conclusion of the Career Track, participants will have developed a strong
knowledge base in both conceptual and applied aspects of machine learning, which they can then
deploy for the benefit of their respective organizations.
I sincerely welcome you to peruse this Brochure and glean more insights into the Career Track
Program in Business Analytics.
Professor &
Chairperson – Analytics and Digital Business Area,
JAGSoM.
JAGSoM is distinguished by a unique Curriculum 4.0 aligned to the
needs of Industry 4.0. The Curriculum is designed to groom ‘T-shaped professionals’ for
new-age roles in new-age companies and is delivered through the unique pedagogy of ‘Learning
by Solving’.
The PGDM program at JAGSoM is delivered by domain specialist faculty, with professional experience in
the industry.
JAGSoM conducted a joint study in collaboration with the National Human Resource Development Network
(NHRDN) to identify the unmet needs of the industry and the skills required for Industry
4.0.
The culmination and analysis of the results of the JAGSoM-NHRDN
study revealed that successful professionals of the future will be ‘T’ shaped professionals,
combining both a l wide breadth of knowledge across areas and in-depth knowledge in a
specialised area.
The main focus of Curriculum 4.0 is to groom T-shaped professionals for new age roles in new age
industries.
As
illustrated in the figure above, the top bar of the ‘T’ represents the broad skills such as
the people skills, the social skills, and an appreciation of multi-functional capabilities
and how the functional areas play among themselves.
The Practice Courses at JAGSoM form an important part of the pedagogical interventions and are
accorded great priority. Almost one third of the total credits in the entire PGDM program is
assigned to practice courses. The Practice Courses are ‘Hands – On’ and serve to ensure that
students get ready for Industry 4.0.
The Majors, Minors and the Career Tracks make up the Vertical bar of the ‘T’.
In order to ensure that studentsare ready for Industry 4.0,Curriculum 4.0 includesCareer Tracks aligned
to their professional goals. Students do a deep dive immersion in their Career Tracks to
acquire the required competencies and critical skills to become industry ready.The Career
Tracks enable the students to achieve depth of knowledge and skills in a specific area
pertaining to Industry 4.0.
The Curriculum 4.0 of JAGSoM includes 4 Majors – Marketing, Finance, Analytics & Digital
Business and HR, as well as 7 Career Tracks in MarTech, Sales &
Service, FinTech, Capital Markets, Banking, Business Analytics and Digital
HR.
JAGSoM’sCurriculum 4.0 is delivered through the unique pedagogy of
‘Learning by Solving’ where students work in groups to solve real life problems supported by
Industry partners and mentored by domain specialist faculty.
The
pedagogy of ‘Learning by Solving’ is operationalized by an integrated pathway consisting of
3 Key Interventions, in which every student at JAGSoM’s PGDM program participates.
Research/Innovation Incubation (RI/II): Students work in small groups either on Research
Projects or Start-Up Ideas, under the guidance of faculty mentors.
In the
Research Incubation practice course, the focus is on research topics that impact practice
where students get an in-depth understanding of various domains through sector and company
analysis. The Innovation Incubation practice course is aimed at developing the
entrepreneurial mindset of students and at providing them a structured path to creating and
launching their own startups.
Career Track Program & Request for Problem (RFP) Project: The same student
groups from the Research Incubation and Innovation Incubation practice courses then move on
to work on a live industry project in the Career Track program. Students select a Career
Track aligned to their professional goals and do a deep dive immersion to acquire the
required competencies and critical skills to become industry ready. Career Tracks are
offered to students in MarTech, Sales & Service, FinTech, Capital Markets, Banking, Business
Analytics and HR-Digital Transformation.
The
‘Request for Problem’ (RFP) project is an integral component of the ‘Career Track’ Program.
Each year, JAGSoM invites industry partners to refer business problems that they are
currently facing which student consulting teams help to solve, each led by a fulltime
faculty member (or an interdisciplinary team of faculty members).
Industry
Internship Program (IIP): The final step in the pathway is a 3-month long Industry
Internship where the students intern with new-age companies to get hands-on experience. The
IIP is an intensive immersion, enabling the students to apply the domain knowledge acquired
in Research/Innovation Incubation, Career Track and RFP projects, while also understanding
real-world industry applications.
A life-skill and lifestyle-oriented course that addresses issues of wellness and essential skills, like communication, negotiation, and cross-cultural orientation, to groom a holistic individual.
Corporate mentors guide students in goal setting and realization of their professional aspirations.
This course aims at enhancing the ability of students to address the challenges of collaboration, conflict resolution, timely and cost-effective execution of critical activities to achieve specific milestones in institution building activities.
Students undertake immersions with NGOs in rural areas. Students learn to design solutions for social problems through a Techno Economic Viability study, thereby enabling sustainable, socially positive, and measurable impact on UN Sustainable Development Goals.
In the Research Incubation practice course, students work in small groups on Research Projects under the guidance of faculty mentors. The focus is on research topics that impact practice where students get an in-depth understanding of various domains through sector and company analysis.Thispractice course is featured by AACSB in the list of best practices in the Asia Pacific.
This course empowers the participants to develop an entrepreneurial mindset and enables them to tackle business problems & challenges through creativity and innovation.Students work in small groups on Start-Up Ideas under the guidance of faculty mentors.The Innovation Incubation practice course is aimed at developing the entrepreneurial mindset of students and at providing them a structured path to creating and launching their own startups.
The PGDM program concludes with this 3-month long internship providing a transitioning bridge between theory and practice. The Industry Internship Program (IIP) is the final component of the RI-RFP-IIP Integrated Pathway for ‘Learning by Solving’.
Introduction to Machine Learning with Python
Advanced Machine Learning
Machine Learning Applications in Marketing
Financial Risk Analytics using Machine Learning
Masterclass on User Generated Content Analytics
Capstone Project
User generated content (UGC) – text, images, speech, and video created by users – from myriad sources such as social media and corporate activities constitutes a large chunk of big data today. Yet, UGC is severely underutilized in decision making, and only recently has attention been focused on the powerful insights and predictions we can generate from user generated content analytics (UGCA). This workshop is designed to showcase the power and potential of UGCA in brand positioning, campaign management, new product design, and a gamut of other managerial applications. Participants will become familiar with important theoretical concepts related to big data such as the long tail of demand, diversity of consumer preferences in a digital world, competitive structure perceived by customers, etc., and learn to use appropriate analytics techniques such as advanced natural language processing, image analytics, and prediction with unstructured data for improved business outcomes.
This is the culmination of the Career Track in Business Analytics. In
this module, students will execute a Capstone project.
Students will be grouped into groups of 3 or 4 members. Each group
will work on a different project.
A few sample project titles which were handled in previous years are
given below:
Each group will work on only one project to be given to them.
Professor, Chairperson - Digital Business & Analytics Area, JAGSoM.
Dr. Supriyo Ghose brings a strong blend of corporate experience and academic excellence. He has an academic and corporate experience of over 25 years, including 14 years in top IT companies and 10 years in higher educational institutions. His corporate affiliations include brands like TCS, PwC, Mahindra Satyam and Infosys, where he played leadership roles in managing and leading mission-critical engagements for international clients. Nearly five years of his industry experience were in the US and Europe.
His research on AI has been presented at international conferences such as AAAI (Association for Advancement of Artificial Intelligence) and WITS (Workshop on Information Systems and Technologies.)
David Bruton Jr. Centennial Chair Professor, McCombs School of Business, University of Texas at Austin.
Dr. Anitesh Barua is the David Bruton Jr. Centennial Chair Professor of Business, Distinguished Fellow of the INFORMS Information Systems Society, Stevens Piper Foundation Professor, University of Texas Distinguished Teaching Professor, and Associate Director of the Center for Research in Electronic Commerce at the McCombs School of Business, the University of Texas at Austin. He received his Ph.D. from Carnegie Mellon University. His research has been supported by both government and private organizations including the National Science Foundation, Cisco Systems, Dell Inc., Ernst & Young, IBM, Intel Corporation, Sprint, Philips, Sybase, and VeriSign.
Deane Malott Professor Emeritus of Management and Professor Emeritus of Marketing and Quantitative methods, Samuel Curtis Johnson Graduate School of Management, Cornell University.
Vithala R. Rao is the Deane Malott Professor Emeritus of Management and Professor Emeritus of marketing and quantitative methods at the Samuel Curtis Johnson Graduate School of Management. He holds master's degrees in mathematical statistics from the University of Bombay and in sociology from the University of Michigan, and a PhD in applied economics and marketing from the Wharton School of the University of Pennsylvania.
He has published over 135 papers on several topics including conjoint analysis and multidimensional scaling, pricing, bundle design, brand equity, market structure, corporate acquisition, and linking branding strategies to financial performance. His current work includes competitive bundling, diffusion of attribute information for new products, and trade promotions. His papers have appeared in the Journal of Marketing Research, Marketing Science, Management Science, Journal of Marketing, and Journal of Consumer Research, among others.
Senior Professor, JAGSoM
Regarded as one of the top Analytics professors in India today, He has a vast experience of more than 34 years in R&D, Academic & Industry in the area of Quantitative Techniques & IT. He teaches courses in the area of IT, Quantitative Techniques, Text Mining and Sentiment Analysis, and Advanced Market Research. He worked as a Professor and Area Chair of Quantitative and Information systems group at IIM Lucknow for about ten years.
Dr. Chandrasekhar has won several awards such as the NASSCOM-DEWANG Mehta award for the best teacher in IT in the Year 2010. He has 2 patents to his credit, having filed patents on ‘Credit Rating Transitions’ and ‘Company Valuation’.
Associate Professor, JAGSoM
A Mathematician by education, Dr. Ganes is a meticulous, persuasive teacher with more than twenty years of diversified experience.
Apart from Statistics and Operation Research, Dr Ganes takes courses based on analytics with a particular interest in Microsoft Excel. He focuses on creating spreadsheet models for real-time business applications with a particular focus on small and medium scale industries and in areas where specialized software tools are not available for data warehousing.
Assistant Professor, JAGSoM
Dr. Ellur Anand is an Assistant Professor in Business Analytics Area. A Green Belt Certified in Lean Six Sigma, his previous academic affiliations include: Alliance University; Kirloskar Institute of Advanced Management Studies (KIAMS); Bapuji Institute of Engineering and Technology for the MBA Program. His industrial affiliations include stints with Perfect Knitters Limited, Richa Global and Shahi Export House.
His current research interests are Predictive Analytics and Machine Learning.
Assistant Professor, JAGSoM
Prof. Shipra Pandey is an Assistant Professor in the Analytics and Digital Business. She has a mathematics background, which has helped her in her research in the area of supply chain risk management in Industry 4.0. She is a reviewer for Benchmarking: An international and International Journal of Consumer Studies.
She has also presented her research at top national and international conferences like POMS, and ISDSI.
Loantap
Automating the Underwriting Process and Data Management
The objective of this project is to develop a scoring model that can help evaluate the employer of a salaried applicant and his relationship with the employer and analyze other variables/inputs relevant for employer verification from an underwriting perspective.
Capgemini
Portfolio Wealth Management
Feasibility of setting up a new line of business in insurance BD.
Capgemini
Credit Fraud Detection.
The project is to build a model to detect credit fraud. The challenge is to recognize fraudulent credit card transactions so that customers of credit card companies are not charged for items that they did not buy. Random data was collected to create a model which will help to detect fraudulent transactions around the world.
Capgemini
Credit Card Acquisition Model.
A model to identify potential customers who are eligible and may apply for credit cards using behavioural patterns and data gathered from social media.
