Business Analytics
Descriptive, predictive and prescriptive analytics for business questions — with the case studies that motivate each.
§ 1Calendar
- Quiz 1—
- Quiz 2—
- End-term examination—
§ 2Previous Year Questions
No papers uploaded yet.
§ 3Resources
Course material2
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Supplementary content (Drive folder)
drive.google.comIITM login required
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Course YouTube channel
youtube.com
Notes3
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Course notes
Notes and study material for this course.
iitmbsdegree.inby IITM BS Students' Corner -
Topic-wise notes
Topic-wise notes on ML Study Hub.
degreedost.comby ML Study Hub -
BA notes on Medium
Written notes for the Business Analytics course.
medium.comby Pratham Bhalla
Syllabus
The official course syllabus, transcribed from the IITM BS Data Science & Applications course booklet.
§ 1Course Description
This course introduces quantitative modelling as a key business skill, focusing on the application of statistical methods. Building on the first-year core course, it demonstrates how these techniques are applied to real business scenarios to derive insights and support optimal decisions.
Lectures-Tutorials-Practicals-Credits: 4-0-0-4
§ 2Faculty
- Prof. Rahul R Marathe, Department of Management Studies, IIT Madras
§ 3Week-by-week
Week 1: Data visualization and storytelling; Design principles; Misleading visuals; Dashboard creation (Excel).
Week 2: Probability distributions; Fitting and goodness-of-fit tests; Distribution identification (visual + statistical, Python).
Week 3: Categorical association; Bayes' rule; Chi-squared test (Python, spreadsheets).
Week 4: Demand and pricing - elasticity, response curves; Linear regression modeling; Python implementation.
Week 5: Demand modeling - constant elasticity, regression; Pricing optimization (revenue, profit); Linear programming; Implementation.
Week 6: Multiple linear regression - interactions, multicollinearity (VIF); Excel and Python implementation.
Week 7: Logistic regression - modeling, evaluation, interpretation; Excel and Python implementation; Application.
Week 8: Efficiency analysis - graphical methods; Data Envelopment Analysis (DEA).
Week 9: DEA - multi-input/output analysis; Efficiency improvement strategies.
Week 10: DEA - efficiency analysis; Conjoint analysis - optimization, ideal point model.
Week 11: Conjoint analysis - linear programming, regression; Excel-based optimization.
Week 12: Business analytics outlook; Chi-square goodness-of-fit (uniform, Poisson).