Business Analytics (BAD714B)

Business Analytics

Course Code BAD714B 
CIE Marks 50
Teaching Hours/Week (L: T:P: S) 3:0:0:0 
SEE Marks 50
Total Hours of Pedagogy 40 
Total Marks 100
Credits 03 
Exam Hours 03
Examination type (SEE) Theory

Module-1

An Overview of Business Intelligence, Analytics, Data Science, and AI: Changing Business

Environments and Evolving Needs for Decision Support and Analytics, Decision-Making Processes and

Computerized Decision Support Framework, Evolution of Computerized Decision Support to

Analytics/Data Science, A Framework for Business Intelligence, Analytics Overview.

Artificial Intelligence - Concepts, Drivers, Major Technologies, and Business Applications:

Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications, Conversational

AI—Chatbots.

[Note: Analytics in action – Excluded]

Chapter 1 (1.2-1.6), Chapter 2(2.4-2.6, 2.9)

Module-2

Descriptive Analytics I -Nature of Data, Big Data, and Statistical Modeling: The Nature of Data in

Analytics, A Simple Taxonomy of Data, The Art and Science of Data Preprocessing, Definition of Big Data,

Fundamentals of Big Data Analytics, Big Data Technologies, Big Data and Stream Analytics, Statistical

Modeling for Business Analytics, Regression Modeling for Inferential Statistics.

[Note: Analytics in action – Excluded]

Chapter 3 (3.2-3.10)

Module-3

Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization: Business

Intelligence and Data Warehousing, Data Warehousing Process, Data Warehousing Architectures, Data

Management and Warehouse Development, Data Warehouse Administration, Security Issues, and

Future Trends, Business Reporting, Data Visualization, Different Types of Charts and Graphs, The

Emergence of Visual Analytics, Information Dashboards.

[Note: Analytics in action – Excluded]

Chapter 4 (4.2-4.11)

Module-4

Predictive Analytics I - Data mining process, methods, and Algorithms: Data Mining Concepts

and Applications, Data Mining Applications, Data Mining Process, Data Mining Methods.

Prescriptive Analytics - Optimization and Simulation: Model-Based Decision-Making, Structure of

Mathematical Models for Decision Support, Certainty, Uncertainty, and Risk, Decision Modeling with

Spreadsheets.

[Note: Analytics in action – Excluded]

Chapter 5 (5.2-5.5), Chapter-8 (8.2-8.5)

Module-5

Predictive Analytics II - Text, Web, and Social Media Analytics: Text Analytics and Text Mining

Overview, Natural Language Processing (NLP), Text Mining Applications, Text Mining Process,

Sentiment Analysis and Topic Modeling, Web Mining Overview, Search Engines, Web Usage Mining

(Web Analytics), Social Analytics.

[Note: Analytics in action – Excluded]

Chapter 6 (6.2-6.10)

Suggested Learning Resources:

Textbook:

1. Ramesh Sharda, Dursun Delen and Efraim Turban, “Business Intelligence, Analytics, Data Science and AI

– A Managerial Perspective”, 5th edition, Global Edition, Pearson Education Limited, 2024.

Reference Books:

1. Steve Williams, Business Intelligence Strategy and Big Data Analytics - A General Management

Perspective, Morgan Kaufmann (Elsevier), 2016.

2. Vincent Charles, Pratibha Garg, Neha Gupta and Mohini Agarwal, Data Analytics and Business

Intelligence - Computational Frameworks, Practices, and Applications, CRC Press, 2023.

3. Ira J. Haimowitz, DATA ANALYTICS FOR BUSINESS - Lessons for Sales, Marketing, and Strategy, Routledge (Taylor & Francis), 2023.