Data-Driven Decision Making

Component

Marks

Description

Mapped CO

Simulations

20

Sim 1 (10M): The Prediction Market. Sim 2 (10M): The Optimization Lab.

CO2, CO3

Case Study

10

"Moneyball Analytics": Analysis of data-driven competitive advantage.

CO1, CO4

Presentation

10

"The Consultant's Report": Presenting data insights to a non-technical client.

CO5

Mid-Term

10

Internal written exam covering Probability & Trees.

CO1

Project

10

"Decision Model": Building a multi-criteria decision model in Excel.

CO2

Participation

10

Active involvement in analytics labs.

All

Session

Topic

Pre-Reading (U. Dinesh Kumar)

Assignment / Case Study

1

Introduction to Decision Making

The DIKW Pyramid (Data, Info, Knowledge, Wisdom).

Ch 1: Intro to Analytics

Assignment: Classify a list of decisions (Strategic vs. Operational).

2

Descriptive Analytics for Consultants

Data visualization and storytelling fundamentals.

Ch 2: Descriptive Stats

Task: Critique a "Bad Chart" vs. a "Good Chart".

3

Probability & Uncertainty

Bayes’ Theorem and decision making under risk.

Ch 3: Probability

Assignment: Solve a Bayesian probability problem.

4

Decision Trees

Mapping choices, chance nodes, and EMV (Expected Monetary Value).

Ch 15: Decision Theory

Task: Construct a Decision Tree for a product launch.

5

The Value of Information

Calculating EVSI (Expected Value of Perfect Information).

Ch 15 (Contd.)

Assignment: Calculate EVSI for a market research report.

6

Sampling & Estimation

Confidence intervals and determining sample size for surveys.

Ch 5: Sampling

Project: Design a sampling plan for a customer survey.

7

Hypothesis Testing in Consulting

A/B testing strategies for client recommendations.

Ch 6: Hypothesis Testing

Case Study: A/B Testing at Netflix.

8

Regression for Driver Analysis

Identifying key drivers of customer satisfaction or sales.

Ch 8: Regression Analysis

Task: Run a regression to find sales drivers.

9

Mid-Term Internal Exam

Assessment of Decision Trees and Stats.

Review: Sessions 1–8

Assessment: Written Exam (10 Marks).

10

Simulation Lab 1: The Prediction Market

Using crowd wisdom and data to forecast outcomes.

Manual: Prediction Sim

Assessment: Simulation Performance Score (10 Marks).

11

Linear Programming (Optimization)

Resource allocation and profit maximization using Solver.

Ch 13: Linear Programming

Assignment: Solve a product mix problem.

12

Simulation Lab 2: The Optimization Lab

Optimizing a logistics network or budget allocation.

Manual: Solver Guide

Assessment: Simulation Performance Score (10 Marks).

13

Multi-Criteria Decision Making (MCDM)

AHP (Analytic Hierarchy Process) for complex choices.

Ref: AHP Basics

Task: Build an AHP model for vendor selection.

14

Monte Carlo Simulation

Modeling risk and uncertainty in financial projections.

Ch 17: Simulation

Assignment: Simulate NPV outcomes for a risky project.

15

Forecasting Techniques

Time series analysis for market sizing.

Ch 11: Forecasting

Task: Forecast demand using Exponential Smoothing.

16

Clustering for Segmentation

K-Means clustering to identify customer personas.

Ch 12: Data Mining

Case Study: Segmentation at Target.

17

Prescriptive Analytics

Moving from "What will happen?" to "What should we do?".

Ch 1: Analytics types

Project: Draft a prescriptive recommendation memo.

18

Ethics in Data Decisions

Algorithmic bias and responsible AI.

Ref: Data Ethics

Assignment: Audit a decision model for bias.

19

Capstone Presentation

Presenting "The Consultant's Report".

Manual: Presentation Rubric

Assessment: Group Presentation (10 Marks).

20

Course Synthesis

The Art and Science of Decisions.

Ref: Thinking, Fast and Slow

Submission: Final Course Portfolio.