Teaching Scheme

Lectures : 3 hrs/week

Examination Scheme

100 marks: Continuous evaluation-

Assignment/Quizzes – 40 marks

End Sem Exam - 60 marks

Topic - 1

• MIS: Big data, its technical challenges and case studies, Big data sample solutions
• Business analytics and case studies; Problems and solutions.

(Motivation and data science in real life)

Topic - 2

• R Programming, examples, and exercises.
• Programming problems and solution with group.

(Tools and technology)

Topic - 3

• MIS: Visualization, in R

(Impact and Appreciation)

Topic - 4

• Smart MIS: Machine learning: Introduction, examples, classification, problems;
• Automated machine learning proposals (R&D) in reputed industries

(State of the art)

Unit 1 (5 Hrs)

Fundamental of Business Analytics

Learning Objectives; What Is Business Analytics? Evolution of Business Analytics, Scope of Business Analytics, Data for Business Analytics, Decision Models, Problem Solving and Decision Making, Spreadsheet Modeling and Spreadsheet Engineering.

Unit 2 (7 Hrs)

Descriptive Analytics

Visualizing and Exploring Data: Data Visualization, Data Queries Using Sorting and Filtering, Statistical Methods for Summarizing Data, Descriptive Statistical Measures: Populations and Samples, Measures of Location, Measures of Dispersion, Measures of Shape, Measures of Association, Statistical Thinking in Business Decisions, Details of Data Modeling.

Unit 3 (7 Hrs)

Predictive Analytics

Predictive Modeling and Analysis: Logic-Driven Modeling, Data-Driven Modeling, Analyzing Uncertainty and Model Assumptions, Model Analysis Using Risk Solver Platform, Introduction to Data Mining: The Scope of Data Mining, Data Exploration and Reduction, Classification, Classification Techniques, Association Rule Mining, Cause-and-Effect Modeling

Unit 4 (7 Hrs)

Prescriptive Analytics

Linear Optimization: Building Linear Optimization Models, Implementing Linear Optimization Models on Spreadsheets, Solving Linear Optimization Models, Graphical Interpretation of Linear Optimization, Using Optimization Models for Prediction and Insight, Applications of Linear Optimization: Types of Constraints in Optimization Models

Unit 5 (4 Hrs)

Making Decisions

Making Decisions with Uncertain Information, Decision Trees, The Value of Information, Utility and Decision Making, Case Study