Business Analytics BAD714B
Course Code: BAD714B
Credits: 03
CIE Marks: 50
SEE Marks: 50
Total Marks: 100
Exam Hours: 03
Total Hours of Pedagogy: 40H
Teaching Hours/Weeks: [L:T:P:S] 3:0:0:0
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.
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.
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.
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.
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.
