Course Outline
Data Science for Managerial Decisions (2025-2027 Batch)
Course Name
Data Science for Managerial Decisions
Module
I
Core/Elective
Core
Credits
3
No. of Sessions
20
No. of Hours
30
Course Instructor:
Name of
Faculty
Email ID
Office
Prof. Purnima Sangle, Prof. Rakesh Verma, Prof. Debabrata Das, Prof. M S
Mahapatra, Prof. Veepan Kumar, Prof. Jasashwi Mandal, Prof. Amit Kumar
Das, Prof. Rony Mitra
purnima@iimmumbai.ac.in, rakeshverma@iimmumbai.ac.in,
debabrata.das@iimmumbai.ac.in, msmahapatra@iimmumbai.ac.in,
veepankumar@iimmumbai.ac.in, jasashwi.mandal@iimmumbai.ac.in,
amitkumardas@iimmumbai.ac.in, rmitra@iimmumbai.ac.in
ALB 5th Floor
Course Description:
This course aims to equip students with the essential data science skills for making informed
business decisions. It covers fundamental statistical and probability concepts, exploratory data
analysis, sampling and sampling distributions, interval estimation and hypothesis testing.
Participants will learn to apply quantitative techniques to analyze business data, interpret
results, and develop data-driven strategies. The course includes hands-on training with realworld business scenarios to ensure practical understanding and application.
Course Learning Outcomes:
By the end of this class students will be able to:
1. Utilize quantitative and qualitative methods, including probability and statistical
analysis, to address real-life business problems and make informed decisions.
2. Perform exploratory data analysis using descriptive statistics and graphical displays to
summarize and interpret data for managerial insights.
1
3. Design effective sampling strategies and conduct hypothesis tests to validate business
assumptions and drive evidence-based decisions.
4. Make data-driven strategies to solve business challenges, demonstrating leadership and
collaboration in diverse and global contexts
Pedagogy:
This course adopts a blended learning approach, combining interactive lectures, case-based
discussions, and hands-on problem-solving sessions. Students will engage with real-world
business cases, apply decision-support tools, and collaborate on analytical exercises to develop
practical decision-making skills.
Textbook/Reference Books
1. Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2018).
Statistics for Business & Economics. Cengage learning.
2. Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of
statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New
York: springer.
3. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know
about data mining and data-analytic thinking. O'Reilly Media, Inc.
Evaluation Scheme:
Component
Weightage
Group Project + Group Presentations
15% + 10%
Mid-term Exam
20%
End-term Exam (Case-based + Conceptual)
40%
Quiz/Assignments/Class Tests
15%
Grading or Evaluation Tools other than Examinations:
•
Class Discussions: Active participation in discussions on assigned readings.
•
Project: Application of statistical modeling techniques in group projects, with details
discussed in class.
•
Assignments: Regular assignments to reinforce the understanding of concepts.
•
Presentations: Group presentations with equitable participation from all members.
•
Class Tests: Periodic tests to assess the understanding of core concepts covered in
lectures.
2
•
Quizzes: Short, topic-specific quizzes to reinforce learning and ensure comprehension
of critical topics.
Group Project:
Students will work in teams to identify a real-world business problem in management, develop
a statistical model, solve and implement it using appropriate data science techniques and
decision-support tools. The project emphasizes practical application, critical thinking, and
collaborative problem-solving.
Industry/Academic Expert Session:
The course includes sessions with industry experts who will share insights on real-world
decision-making challenges and the application of data science techniques in management.
This session bridges the gap between academic learning and industry practices, offering
students valuable practical perspectives.
3
Detailed Session Plan
Session
No.
1
2–4
5–6
7–10
Topic
Case Studies
Introduction
• Applications of Quantitative
Techniques in Business
• Data – Categorical & Quantitative
• Scales of Measurement
Exploratory Data Analysis
• Descriptive Statistics (Tabular,
Graphical, Numerical)
• Measures of Location and
Variability
• Box Plots, Five-Number Summary
• Outlier detection
• Measures of Association
Introduction to Probability
• Events, Counting Rules,
Conditional Probability
• Bayes’ Theorem
Probability Distributions
• Discrete: Binomial, Poisson,
Hypergeometric
• Continuous: Normal, Uniform,
Exponential
Histograms and the Normal
Distribution in Microsoft
Excel, HBS Case W16413PDF-ENG
Nata Supermarkets:
Customer Analytics (only
EDA Part), HBS Case
W33834-PDF-ENG
Additional Readings (Research
Papers / Consulting Reports /
Web Sources)
-
-
Ratnagiri Alphonso Orchard:
Bayesian Decision Analysis,
HBS Case W16203-PDFENG
Lorex Pharmaceuticals, UVA Case UV0347-PDF-ENG
-
Introduction to Statistical
Learning and Exploratory Data
Analysis
Introduction to Statistics
Assignment
Assignment
I
On the Theory of Scales of
Measurement
Application of Probability
Theory in Small Business
Management
On a Probability Distribution in
Supply Chain Management
Probability Distributions for
Modeling Stock Market Returns
- An Empirical Inquiry
Quiz /
Class
Test
Quiz I
Assignment
II
Class
Test I
4
11–13
14–16
17–20
Sampling & Sampling
Distributions
• Point Estimation, Sampling
Distributions
Interval Estimation
• Confidence Intervals for Means (σ
known & unknown)
• Determining Sample Size
Hypothesis Testing
• Null & Alternative Hypotheses
• Type I & II Errors
• One-/Two-Tailed Tests
Testing Marketing
Hypotheses at WSES, IIMA
Case IMB693-PDF-ENG
-
Concept of the Sampling
Distribution of the Mean
-
The Role of the Sampling
Distribution in understanding
Statistical Inference
Assignment
III
-
Purposeful
Sampling
and
Saturation
in
Qualitative
Research
Methodologies:
Recommendations and Review
-
Introduction to Confidence
Intervals
Quiz II
-
Introduction to Hypothesis
Testing
Class
Test II
5
Rubrics for Evaluation
Scale / Criteria
Excellent
Good
Fair
Poor
Conceptual
Deep understanding and
Good grasp with minor Partial understanding,
Lacks understanding
Understanding
strong theory-practice links
gaps
weak link to practice
and application
Application of Models
Accurate use of tools and
Mostly correct use
Significant errors in
Incorrect or no model
formulation
use
Shallow analysis
No analysis or insight
Poor communication
models
Problem Solving &
Insightful solutions with
Basic analysis and
Analysis
critical analysis
reasonable solutions
Communication &
Clear, professional, visually
Clear with minor
Some clarity but lacks
Presentation Skills
supported
issues
engagement
6