Data analysis and Business Modelling - MB25204

 

 

MB25204:

Data Analysis and Business Modeling (Laboratory)

 

Course Objectives:

This course is designed to provide practical knowledge and hands-on experience in applying data analysis techniques for solving Business problems. The learners will be equipped with the use of spreadsheet tools and analytical software for descriptive statistics, hypothesis testing, forecasting, and modeling. Emphasis is laid on Business applications like risk analysis, revenue management, and optimization models using real-world data. The course integrates advanced decision-making techniques and prepares learners for data-driven strategic and operational decision-making in various functional domains.

 

Data Description and Hypothesis Testing

Descriptive statistics – Data visualization – Measures of central tendency and dispersion – Parametric tests (t-test, ANOVA) – Non-parametric tests (Chi-square, Mann–Whitney U test) – Interpretation using spreadsheet software or R/Python.

 

Correlation, Regression and Forecasting

Bivariate and multivariate correlation – Simple and multiple regression analysis – Interpretation of coefficients – Forecasting using time series data – Moving averages – Exponential smoothing – Real-time forecasting models.

 

Financial Modeling for Business Decisions

Portfolio selection – Scenario analysis – Sensitivity analysis – Risk and return measures – Application in investment decision-making – Simulation techniques in Excel.

 

Revenue Management and Optimization

Revenue maximization principles – Pricing decisions – Capacity allocation – Data modeling for revenue optimization – Real-life case modeling in Excel / Python.

 

Operations Research Models for Business

Linear programming using Solver – Transportation models – Assignment problems – Network models – Application of Queuing theory – Waiting line models – Inventory control models.

 

Integrated Lab Projects and Advanced Excel Tools

Use of Solver, Data Analysis Toolpak, Power Query – Mini project integrating multiple models – Dashboard preparation – Report generation and decision support system development using spreadsheet / analytics software.

 

Note: Spreadsheet Software and Data Analysis Tools

 

References:

1. David R. Anderson et al., An Introduction to Management Sciences, South-Western College, 16th Ed., 2024

2. William J. Stevenson & Ceyhun Ozgur, Introduction to Management Science with Spreadsheet, McGraw-Hill, 2009

3. Hansa Lysander Manohar, Data Analysis and Business Modeling using Microsoft Excel, PHI, 2017

4. David M. Levine et al., Statistics for Managers using MS Excel, Pearson, 9th Ed., 2021

5. Albright, Winston & Zappe, Data Analysis for Managers with Microsoft Excel, Cengage, 6th Ed., 2019

6. Wayne L. Winston, Microsoft Excel Data Analysis and Business Modeling, Pearson / Microsoft Press, 7th Ed., 2021

 

 

Course Outcomes

After completing this course, the student will be able to: 

● CO1: Demonstrate conceptual knowledge of descriptive statistics, hypothesis testing, regression analysis, financial modeling, optimization techniques, and integrated dashboard creation for data-driven business decisions.

● CO2: Interpret and relate descriptive statistics, regression models, time series data, financial modeling concepts, operational research models, and advanced Excel tools to understand business problems and analytical solutions.

● CO3: Apply statistical techniques, regression analysis, time series models, financial modeling concepts, and optimization methods to analyze data, perform forecasting, and solve business problems.

● CO4: Analyze business data using statistical tests, correlation and regression analysis, and operational research models to interpret relationships, forecast outcomes, and evaluate potential solutions.

● CO5: Evaluate various analytical models and techniques, including financial, revenue management, and operations research models, to make informed decisions and optimize business performance.

● CO6: Develop integrated analytical solutions, including dashboards and reports, by applying statistical methods, forecasting models, and optimization techniques to support data-driven decision-making in a comprehensive business context.