Department of Management Sciences (DoMS)

*Initially [ Industrial and Management Engineering (IME) ]

Programme Name: Data Science and Business Analytics (DSBA)

Module

Module Title

Credit

Description

Content

MBA931

Stochastic

Elements of

Business

This course provides the foundation required for

the analysis of stochastic elements of business. It

aims to develop basic understanding about data

and their analysis for solving business problems,

through the study of the underlying principles of

probability and statistics.

● Data-driven decision making

○ Some examples involving stochastic

elements.

● Probability

○ Basic concepts of probability,

Conditional probability and

independence.

● Random variables

○ Mass, distribution and density

functions, Common random

variables, Functions of a random

variable, Mean, variance and

quantiles.

● Random vectors

○ Joint distribution, Conditional

distribution and independence, Sum

of random variables, Covariance and

correlation, Limit theorems.

● Statistical estimation

○ Population and sample, Point

estimation, Goodness properties of

point estimators, Interval estimation,

Confidence intervals.

● Hypothesis testing

○ Introduction to hypothesis testing,

Some hypothesis tests.

MBA932

Linear and Non-

Linear Modeling

The purpose of this module is to understand

regression tools that allow the students to explore

causal relationships between different factors

within a business environment. It is expected that

students taking this module will gain skills and

experiences in data analysis, economic modeling

and interpretation of results.

● Economic Data and Insights Review of

Probability

● Review of Statistics, Hypothesis testing,

Confidence Intervals

● Linear Regression with one regressor,

Classical linear Model, Assumptions, OLS

estimator

● Hypothesis testing for linear Regression with

one regressor, measures of fit, dummy

variables,heteroskedas- ticity

● Linear Regression with many variables,

Omitted variable Bias

● Multiple Regression, Multicollinearity,

Control Variables, Measures of fit

● Common Pitfalls in Regression Analysis,

Non Linear Models

● Regression with Binary Dependent

variables, Logit, Probit

MBA933

Data Mining Tools

& Techniques

The module introduces the fundamental

approaches to knowledge discovery and data

mining (DM) and its main theoretical foundations.

Starting with exploratory data analysis, the

● Introduction to DM, DM Tools, DM

functionalities and applications

● Basic Data Understanding

● Data Preparation for DM

module will present fundamental algorithms for

data preprocessing, classification and prediction

problems. Emphasis will be to demonstrate these

techniques to analyze real-life problems. There is

significant importance to interpret the result/

knowledge obtained from these algorithms.

● Supervised and Unsupervised Learning

● Decision Trees

● Artificial Neural Network

● Naive Bayes Classifier

● Classifier Evaluation and Improvement

Techniques

MBA934

Applied Machine

Learning

Organizations are making more and more data-

driven decisions for improving their processes,

identifying opportunities and trends and

launching new products. With the advent of

machine learning techniques and the availability

of data and high computing capabilities, data

driven decision-making has transformed

considerably. This module aims to understand

popular machine learning (ML) algorithms

(Regression, Classification and Clustering) and

their business applications to appreciate these

algorithms .Emphasis will be on ML applications

with real-world decision making.The course

allows hands-on experience in implementing the

most widely employed algorithms in business

domains using machine learning libraries, mainly

in Python.

● ML for Data Science

○ Introduction and Fundamentals of

ML, Supervised and Unsupervised

Learning Algorithms, Fundamentals

of R programming, Statistical

modeling, inferential statistics,

confidence interval estimation,

hypothesis testing

● Exploratory Data Analytics

○ Data cleaning and data visualization,

generating insights from data

● Predictive Analytics with Linear

Regression modeling

○ Simple and multiple linear

regression, residual diagnostics,

multicollinearity, heteroscedasticity

etc.

● Time Series Analytics

○ ARIMA models, Time series

stationarity, Unit roots, Modelling

short-term and long-term

relationships

● Panel Data models

○ Fixed effects and Random effects

models, Least Square Dummy

Variable models

● Non-Linear models

○ Logistic Regression, Quantile

Regression, Model Building, and

Estimation issues

● Big Data Text Analytics

○ Natural Language Processing, Text

Mining, Sentiment Analysis, Text

corpus visualization, Case study

example

MBA935

Optimization

Methods for

Analytics

The course aims to prepare the candidates in

optimization models and techniques to solve

business problems.

● Introduction to operations research: Linear

programming— formulation.

● Linear programming: Solution procedures-

graphical and simplex methods.

● Linear programming: Duality and sensitivity

analysis.

● Linear programming formulations:

Transportation and assignment problems

● Multi-objective optimization: Modeling,

solution approaches, applications

● Integer programming: Modeling and

applications

● Nonlinear programming—Unconstrained

optimization technique, applications

● Nonlinear programming—Constrained

optimization techniques, applications

MBA936

Temporal and

Cross-Sectional

Modeling

The aim of this course is to introduce the

principles of forecasting and time series analysis

to business students. It is an applied course with

● Introduction to time-series data - time-series

graphics

● Forecaster’s toolbox

focus on building models using time-series

data.The main aim of the course is to equip

students with forecasting tools in business

settings.

● Time-series Decomposition

● Moving Average and Exponential Smoothing

● Time-series regression

● ARIMA models

● Dynamic regression models

● Advanced forecasting methods – VAR,

Neural Network

MBA937

Causal Inference

Models

The module introduces a set of econometric tools

to draw causal inferences in a social/organization

setting. A critical objective of the module is to

emphasize the importance of conducting cause

and effect analysis in policy-related decision-

making, the problems that occur while conducting

such analysis in a managerial context and then to

learn the relevant methods that can help

overcome these constraints .Emphasis of the

module will be on applying these tools/methods

to various managerial/policy-related decision

problems.

● Introduction

● Refresher on multivariate

regression,focusing on dummy variable

regression and interpretation of results.

Interpretation of interaction effect.

● From correlation to causality. True

Experiments and Quasi- experiments.

● Matching methods.

● Fixed effects and Event studies

● Difference-in-Differences method (DID).

● Regression Discontinuity Design (RDD).

● Instrumental Variable Regression (IV).

● Project Presentations

MBA938

Multivariate Data

Analysis

The main aim of this course is to recognize the

patterns within multivariate data. This course will

focus on interdependence relationships rather

than dependence relationships.These techniques

may be useful for categorizing individual entities

into consumer segments, or uncovering latent

variables that cannot be measured directly.

● Introduction to R-packages,Introduction to

Model Building with Multivariate Data

● Visualizing and Preparing MV Data for

Analysis

● Principal Component Analysis; Factor

Analysis

● Association, Canonical Correlation Analysis

● Conjoint Analysis

● Cluster Analysis; Discriminant Analysis

● Multidimensional Scaling; Correspondence

Analysis

● Structural Equation Modeling

MBA939

Financial Analytics

This course aims to provide the students with the

technical knowledge of building financial models

and doing financial analytics in Excel/R/Python in

corporate finance and financial markets. The aim

is to bridge the gap between financial theory and

practice. The course includes topics covering the

application of data analytics in the financial

markets: equity markets, fixed-income markets

and derivative markets. The module has a

dedicated focus on portfolio analytics and risk

management.

● Course introduction and Excel

preliminaries

● Analytics in Equity Markets

○ Time Value of Money

○ Dividend discount model

○ Discounted Cash Flow approach

○ Incorporating assumptions in the

Valuation Model using Excel

● Analytics and report generation for

Equity Research and Investment banking

● Analytics of Fixed income markets

○ Pricing of bonds

○ Term structure modeling in

Excel/R/Python

○ Fixed-income risk measurement and

management: Duration, convexity

○ Fixed-income portfolio analytics

● Portfolio Management

○ Building Basic Portfolio Model using

Excel/R/Python

○ Performance measurement analytics

using Excel/ R/ Python

○ Portfolio analytics and dashboards

using R, Python, R- Shiny

● Analytics of Derivative markets

○ Derivative Pricing Models: Forwards,

Futures, Options, and Swaps

○ Derivatives trading strategy and

performance analytics

○ Derivative and risk management

using R, Python, R-Shiny

MBA940

Marketing

Analytics

Given the availability of large amounts of retail

data related to individual’s shopping and online

browsing behavior, today’s marketing strategies

are completely data-driven. The aim of this

course is to understand the use of statistical tools

to improve marketing decisions and return on

marketing investment.

Students will learn:

(i) The advantages of quantitative marketing,

(ii) Apply metrics-driven techniques to improve

marketing decisions

(iii) Learn by doing through computer based

models.

● Introduction to Marketing Analytics,

Summarizing marketing data

● Understanding customer requirement –

conjoint analysis, logistic regression,

discrete choice analysis

● Pricing – estimating demand curve,

optimizing price, price bundling, non-linear

pricing, price skimming and sales, revenue

management

● Customer lifetime value (CLV) – calculating

CLV, using CLV to value a business, Monte

Carlo simulation, optimizing customer

acquisition and retention

● Market segmentation – cluster analysis

● Retailing – market basket analysis, RFM

analysis, optimizing direct mail

campaigns,allocating retail space and sales

resources

● Advertising – measuring the effectiveness of

advertising, media selection models, pay per

click online advertising

● Online Business, Recommender Systems

MBA941

Supply Chain

Analytics

To provide an understanding of the design and

management of a supply chain.To will enable one

to critically analyze the performance of a supply

chain and will give exposure to the techniques for

improving the performance of a supply chain.

● Supply chain network design

○ mixed integer linear programming

models on network design

● Supply chain inventory optimization

● Supply chain dynamics

○ strategies to mitigate information

distortion and bullwhip effect

● Modeling and analysis of the waiting

lines

● Contract design in the supply chain

○ achieving supply chain coordination

through contracts using stochastic

non-linear optimization models

● Supply chain resilience and role of

technology in supply chain management

MBA942

HR/Human

Resource Analytics

This course aims to impart necessary skills for

quantification of human attributes and efforts and

measurement of its efficiency and effectiveness

in producing desirable organizational outcomes.

It also provides modalities for analyzing and

drawing data driven insights for determination of

developmental needs of employees and

designing total reward system.

● Introduction to key OB and HRM processes

● Challenges in quantification of amorphous

human attributes

● HR analytics: From benchmarking to

predictive analytics

● HR metrics: Design, reliability and validity,

concept of RoIHR

● HR analytics approach: Identification,

measurement, analysis and interpretation

● Alignment of business and HR objectives

through analytics

● Talent Management through HR Analytics:

Audits and competency repository

● HR analytics amid emerging work

arrangements (WFH and digitalization)

MBA943

Social Media

Analytics

The access to social media has changed the way

individuals live, buy, interact with each other and

consume products, services and

information.Individuals are more connected with

each other than ever before and this

interconnectivity leads to large consequences of

● Challenges in social media: Online

experiments, Customer decision journey,

recommendation and personalization,

analysis of text and network data

● Application of social data, basics of network

analysis, network visualization, hands on

small events known as the “butterfly effect”. The

aim of this Module is to understand the

complexity of network effects and to be able to

use online data about social media use to enable

companies to drive their strategies and make

profitable decisions.

with Gephi

● Representing and measuring networks:

strength of weak ties, centrality-degree,

diameter, path lengths

● Prestige, influence: Betweenness,

PageRank, Eigenvector, Bonacich, decay,

closeness, centrality

● Analysis of real world networks

● Recommendations in social media

MBA944

Project 1

To allow the students to apply the learnings from

the modules to address data analytics and

business problems.The students are expected to

work for a quarter on this project and derive

meaningful insights from its execution. The

Projects are expected to showcase the problem-

solving capabilities and skills of the students.

Capstone Project

MBA945

Project 2

To allow the students to apply the learnings from

the modules to address data analytics and

business problems.The students are expected to

work for a quarter on this project and derive

meaningful insights from its execution. The

Projects are expected to showcase the problem-

solving capabilities and skills of the students.

Capstone Project