| MBA931 |
Stochastic Elements of Business |
5 |
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.
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- ● 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.
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| MBA932 |
Linear and Non-Linear Modeling |
5 |
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.
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- ● 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: Measures of fit, Dummy variables, Heteroskedasticity
- ● 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
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| MBA933 |
Data Mining Tools & Techniques |
5 |
The module introduces the fundamental approaches to knowledge discovery and data mining (DM) and its main theoretical foundations. Starting with exploratory data analysis, the 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.
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- ● Introduction to DM: DM Tools, DM functionalities and applications
- ● Basic Data Understanding
- ● Data Preparation for DM
- ● Supervised and Unsupervised Learning
- ● Decision Trees
- ● Artificial Neural Network
- ● Naive Bayes Classifier
- ● Classifier Evaluation and Improvement Techniques
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| MBA934 |
Applied Machine Learning |
5 |
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.
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- ● 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, Modeling 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
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| MBA935 |
Optimization Methods for Analytics |
5 |
The course aims to prepare the candidates in optimization models and techniques to solve business problems.
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- ● 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
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| MBA936 |
Temporal and Cross-Sectional Modeling |
5 |
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 focus on building models using time-series data. The main aim of the course is to equip students with forecasting tools in business settings.
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- ● Introduction to time-series data - time-series graphics
- ●Forecaster’s toolbox
- ●Time-series Decomposition
- ● Moving Average and Exponential Smoothing
- ●Time-series regression
- ● ARIMA models
- ●Dynamic regression models
- ●Advanced forecasting methods – VAR, Neural Network
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| MBA937 |
Causal Inference Models |
5 |
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.
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- ●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
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| MBA938 |
Multivariate Data Analysis |
5 |
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.
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- ● 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
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| MBA939 |
Financial Analytics |
5 |
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.
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- ● 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
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| MBA940 |
Marketing Analytics |
5 |
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.
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- ● 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
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| MBA941 |
Supply Chain Analytics |
5 |
To provide an understanding of the design and management of a supply chain. To enable one to critically analyze the performance of a supply chain and provide exposure to techniques for improving supply chain performance.
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- ● 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
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| MBA942 |
Human Resource Analytics |
5 |
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.
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- ● 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)
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| MBA943 |
Social Media Analytics |
5 |
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 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.
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- ● 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 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
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| MBA944 |
Project 1 |
5 |
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.
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Capstone Project
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| MBA945 |
Project 2 |
5 |
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.
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Capstone Project
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