Business Data Management
A significant source of data sets and problems for data scientists will come from the business domain. This course provides a basic understanding of how businesses are organised and run from a data perspective.
Course ID: BSMS2001
Course Credits: 4
Course Type: Data Science
Recommended Pre-requisites: None
What you’ll learn VIEW COURSE VIDEOS
Course structure & Assessments
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.
| WEEK 1 | Consumption and demand: Micro & Macro economics: the role of data, production, consumption and exchange, consumption baskets, sources of consumer survey data |
| WEEK 2 | Micro-economic concepts: Utility: cardinal vs ordinal, indifference curves. Demand and supply curves, changes in demand and elasticity. production cost, cost curves. Make vs buy decisions, production quantity decisions |
| WEEK 3 | Firm level strategies and performance data: Objectives and types of pricing strategies, analysis of firm performance - key ratios. Analysis examples: Ultratech, Page Industries, Nestle, TCS |
| WEEK 4 | Analysing industry level data: Industry definition and classification codes, IIP and PMI, industry market structure and concentration indices, competitive positioning in an industry - Porter's five forces. Analysis examples: Cement industry, Textile industry, FMCG industry, IT industry |
| WEEK 5 | Case study 1 - Fabmart (E-Commerce): Introduction to E-Commerce, Fabmart case introduction, explanation of data set & questions to be answered, revenue pareto, volume pareto, scatter plot of sales and revenue, revenue trend |
| WEEK 6 | Fabmart case continued: Sales analysis, organisation of distribution centre, analysis of sales trends, average days of inventory, ledger, avoiding stockouts |
| WEEK 7 | Case study 2 - Ace Gears (Manufacturing): Introduction to the manufacturing sector, context of the automotive industry during the years 2019-2021, explanation of data set containing monthly information on sales, production, inventory and costing. Revenue trend analysis, portfolio management |
| WEEK 8 | Ace Gears case study continued: Regional sales analysis, sales agent planning, production scheduling, scrap analysis, unit level profitability analysis, raw material re-ordering and safety stock |
| WEEK 9 | Case study 3 - Tech Enterprises (IT): Introduction to HR as a function, Introduction to the Tech Enterprises, internal sourcing, ranking of internal candidates, job description, sourcing channels and their analysis, recruitment process and onboarding |
| WEEK 10 | Case study 4 - PayBuddy (Fin Tech): Introduction to Finance Industry and Fintech, payment processing and money flow, new credit product introduction, nudge economics, payment transaction and customer data set, identifying rules to target the appropriate customers |
| WEEK 11 | Paybuddy case continued: Introduction to A/B testing, analysis of the A/B testing data, credit risk evaluation, risk-return tradeoffs |
| WEEK 12 | Discussion on student acquired data sets. Wrap upaWrap up (summary) of the case studies, course project work |
About the Instructors
Dr. Venkatesh is a Professor of Practice at IIT Madras, where he is involved with several projects in the field of education. He is also a Fellow and Director of Sasken Communication Technologies Ltd, a leading Indian R&D services provider, and a founder of Mylspot, an education technology startup that aims to bridge knowledge gaps of students through a mentored learning platform.
Dr Venkatesh is a graduate in electronics from IIT Madras, PhD in Computer Science from TIFR, Mumbai and was a faculty member of the Computer Science & Engineering Dept of IIT Bombay for 8 years where his research interests revolved around declarative languages and their application to the design of embedded systems. He moved to the industry when Sasken was being formed, where he led their technology activities for over two decades. He was a visiting/adjunct faculty at IIM Bangalore for 10 years and a Chair Professor in the department of EE at IIT Madras for 3 years.
Dr. Venkatesh was elected as a fellow of the Indian National Academy of Engineers in 2006 and the IETE in 2012. He won the Technomentor award of the Indian Electronics and Semiconductor Association in 2013 and the Vasvik award for industrial research in 2015. Dr. Venkatesh serves on a number of government, industry and investor committees and boards in the areas of microelectronics, telecommunications and education.
Other courses by the same instructor: BSCS1001 - Computational Thinking and BSMS2002 - Business Analytics
Suresh Babu has completed his M.S. in Development Economics and M.Phil. in Applied Economics before completing his Ph.D. from Jawaharlal Nehru University, New Delhi. He currently is a professor at the Department of Humanities and Social Sciences, and his key areas of research are in applied macroeconomics, trade & development and industrial economics.
Milind is the Chief Program Officer at the Center of Excellence for Machine Intelligence and Robotics, IIIT Bangalore. Prior to joining IIITB, Milind has 26 years of experience in the corporate sector, first with Sasken Communication Technologies and most recently with Tata Elxsi as the head of the Systems Business Unit. Milind has lead projects in Semiconductor, Automotive, Communications and Smart Home verticals.
Milind has a PhD in Computer Science and Engineering from IIT Bombay. Before his PhD, Milind obtained a B.Tech and an M. Tech also from IIT Bombay. His thesis was on Abstract Interpretation of Functional Programming Languages. Milind also has a Diploma in Practicing Management from INSEAD, France.
Milind’s primary research interests are AI (Natural Language Processing, Mathematical Logic, Hardware acceleration for AI at the Edge) and Interaction between Society and Technology.