Master of Management Studies (M.M.S) Business Analytics Syllabus - Mumbai University 2026

Course objectives

• 1 To introduce basic terminologies, analytical tools, and platforms relevant to business analytics.
• 2 To train students in applying descriptive statistics and predictive techniques using Excel and R.
• 3 To develop competence in using data mining models such as decision trees and clustering for insights.
• 4 To enable students to evaluate business scenarios using time series forecasting and visualization tools.
• 5 To guide learners in constructing data-driven models for business decisions using real-time datasets.

Course outcomes

• CO1: RELATE the basic terminologies related to the concepts of Business Analytics
• CO2: UNDERSTAND use of various tools of Business Analytics for making justifiable business decisions
• CO3: CHOOSE appropriate technique for data mining for providing appropriate business solutions
• CO4: ANALYZE the available data and recommend appropriate analytical techniques
• CO5: COMPARE and visualize the results for fact-based decision-making
• CO6: CONSTRUCT appropriate predictive models based on available information

Unit 1 CO1, CO2 · 3 hours

Introduction to Analytics Meaning, application areas of business analytics, techniques of analytics.

Unit 2 CO2, CO3 · 3 hours

Statistics for Business Analytics Central tendencies and dispersion, central, limit theorem, sampling distribution, hypothesis testing, simple linear regression, categorical data analysis, analysis of variance (ANOVA), non-parametric tests.

Unit 3 CO3, CO4 · 3 hours

Advanced Excel Proficiency (Practical) Describing Numeric Data, Pivot Table Analysis, Linear Regression, Comparing Two Sample Variances, Comparing Two Sample Means, Pair T Test, One Way ANOVA, Two Way ANOVA, Generating Random Numbers, Rank and Percentile, Histogram Procedure, Exponential Smoothing and Moving Average, Sampling,Covariance and Correlation, Goal Seek and Solver.

Unit 4 CO1, CO2 · 3 hours

Understanding R Using R Studio, working with data in R, R procedures.

Unit 5 CO2, CO3 · 3 hours

Data Mining using Decision Tree Introduction to decision trees, model design and data audit, demo of decision tree development, algorithm behind decision tree and other decision tree.

Unit 6 CO3, CO4 · 3 hours

Data Mining using clustering in R Understanding cluster analysis using R, clustering as strategy, hierarchical clustering, non-hierarchical clustering - K means clustering, variants of hierarchical clustering, different distance and linkage functions.

Unit 7 CO3, CO4 · 3 hours

Time Series Forecasting Time series vs causal models moving averages, exponential smoothing, trend, seasonality, cyclicity causal modelling using linear regression forecast accuracy.

Unit 8 CO3, CO4 · 3 hours

Predictive Modelling – Logistic Regression using R Data import and sanity check, development and validation, important categorical variable selection, important numeric variable selection, indicator variable creation, stepwise regression, dealing with multicollinearity, logistic regression score and probability, KS calculation, coefficient stability check, iterate for final model.

Unit 9 CO3 · 3 hours

Overview of Big Data and Hadoop Big data and Hadoop and concept, application, cloud computing, generators of big-data.

Unit 10 CO4, CO5 · 3 hours

Data Analysis & Visualization Credit risk analytics, fraud risk analytics, financial services marketing analytics, Data Visualization using Power BI and tableau tools

Text Books

• 1 Business Analytics: Data Analysis & Decision Making, Authors: S. Christian Albright & Wayne L. Winston, Publisher: Cengage Learning
• 2 Data Science for Business, Authors: Foster Provost & Tom Fawcett, Publisher: O'Reilly Media
• 3 Business Analytics: The Science of Data-Driven Decision Making, Author: U. Dinesh Kumar, Publisher: Wiley India