Master of Management Studies (M.M.S) Data Mining and Business Intelligence Syllabus - Mumbai University 2026

Course objectives

• 1 To introduce core concepts, methodologies, and tools in data mining and business intelligence.
• 2 To equip students with practical skills in predictive analytics, classification, clustering, and data visualization.
• 3 To develop analytical capabilities for leveraging data insights for strategic business decision-making.
• 4 To foster understanding of ethical and privacy considerations in data mining practices.
• 5 To cultivate practical knowledge of contemporary tools and platforms used in business intelligence.

Course outcomes

• CO1: Understand the introduction to data mining: introduction, definition of data mining, data mining parameters, how data mining works?
• CO2: Identify the data mining techniques, statistical perspective on data mining, statistics-need and algorithms focused on business intelligence
• CO3: Analyze the business intelligence essentials.
• CO4: Apply the Business Intelligence and Data Mining tools and its application for Business
• CO5: Design Data mining and BI Strategy for effective business,

Unit 1 CO1 · 3 hours

Introduction to Data Mining: Introduction, Definition of Data Mining, Data mining parameters, How Data Mining works? Types of relationships, Architecture of Data

Unit 2 CO1 · 3 hours

Classification on Data Mining system, Various risks in Data Mining, Advantages and disadvantages of Data Mining, Ethical issues in Data Mining, Ethical issues

Unit 3 CO1, CO2 · 3 hours

Data Mining Techniques: Introduction, Statistical Perspective on Data Mining, Statistics-need and algorithms

Unit 4 CO1, CO2 · 4 hours

Business Intelligence an Introduction: Introduction, Definition, History and Evolution, BI Segments, Difference between Information and Intelligence, Defining Business Intelligence Value Chain, Factors of BI System, Real time Business Intelligence, BI Applications.

Unit 5 CO3 · 4 hours

Business Intelligence Essentials: Introduction, Creating BI Environment, BI Landscape, Types of BI, BI Platform, Dynamic roles in BI, Roles of Business Intelligence in Modern Business- Challenges of BI.

Unit 6 CO4 · 3 hours

Multiplicity of BI Tools, Types of BI Tools, Modern BI, the Enterprise BI, Information Workers and its applications

Unit CO4, CO5 · 5 hours

Business Intelligence Life Cycle: Introduction, Business Intelligence Lifecycle, Enterprise Performance Life Cycle Framework, Life Cycle Phases, Human Factors in BI Implementation, BI Strategy, Business Intelligence Issues and Challenges: Introduction, Critical Challenges for Business Intelligence success. Application of Business Intelligence and Data Mining for Business.

Unit 7 CO4, CO5 · 5 hours

Emerging Trends of Data Mining and Business Intelligence with case studies

Textbooks

• 1 Introduction to Data Mining with Case Studies by G K Gupta.
• 2 Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Education, 2007
• 3 Insight into Data Mining Theory and Practice by K. P. Soman, Shyam Diwakar and V. Ajay. Prentice Hall of India, 2006.
• 4 Introduction to Data Mining with Case Studies by G. K. Gupta. Prentice Hall of India, 2006.
• 5 Data Mining Methods and Models by Daniel T. Larose. Wiley-Interscience, 2006

Reference Books

• 1 E-commerce from Vision to Fulfilment by Elias M. Awad. PHI, 2002.
• 2 Digital Business and E-Commerce Management by Dave Chaffey, 2014.
• 3 Introduction to E-Business-Management and Strategy by Colin Combe. ELSVIER, 2006.
• 4 Digital Business Concepts and Strategy by Eloise Coupey. Pearson.
• 5 Trend and Challenges in Digital Business Innovation by Vinocenzo Morabito. Springer.
• 6 Digital Business Discourse by Erika Darics. Palgrave Macmillan, 2015.