Master of Management Studies (M.M.S) Advanced Database and Data Warehousing Syllabus - Mumbai University 2026

Course objectives

• 1 To provide comprehensive knowledge of advanced database concepts, models, and architectures.
• 2 To equip students with skills in designing, implementing, and managing data warehousing solutions.
• 3 To develop practical abilities in data integration, ETL processes, and business intelligence applications.
• 4 To foster analytical capabilities for evaluating database performance, security, and compliance.
• 5 To cultivate strategic insights into emerging database technologies and big data management practices.

Course outcomes

• CO1: Understand the data warehousing components –building a data warehouse.
• CO2: Describe the framework of the structure of the data warehouse, granularity.
• CO3: Apply normalization and denormalization for operational access of data warehouse.
• CO4: Analyze the distributed data warehouse, the local and global data warehouse, undistributed data warehouse and development.
• CO5: Develop the multidimensional data model

Unit 1 CO1, CO2 · 5 hours

Data warehousing components –Building a data warehouse, DBMS schemas for decision support – data extraction, clean-up, and transformation tools, monitoring the data warehousing environment.

Unit 2 CO2, CO3 · 5 hours

The structure of the data warehouse, Granularity, partitioning as a design approach, structuring data in data warehouse, data homogeneity and heterogeneity, incorrect data in data warehouse

Unit 3 CO3 · 4 hours

Normalization and de normalization, Triggering the data warehouse record, managing volume, direct operational access of data warehouse data, levels of granularity.

Unit 4 CO4, CO5 · 4 hours

Data warehouse technology: Managing multiple media, interface to emerging technologies, and management of metadata, multidimensional DBMS and data warehouse.

Unit 5 CO3, CO4 · 4 hours

Types of data warehouse: The distributed data warehouse, the local and global data warehouse, undistributed data warehouse and development.

Unit 6 CO4 CO5 · 4 hours

Management analysis: Cost justification and return on investments for a data warehouse, corporate information compliance and data ware housing, data warehousing, design review checklist and relevance.

Unit 7 CO4, CO5 · 4 hours

Application areas of data warehousing and business issues and challenges with case studies

Textbooks

• 1 Data Warehousing, Data Mining and OLAP by Alex Berson and Stephen J. Smith. Tata McGraw–Hill Edition, 2007
• 2 Data Mining Concepts and Techniques by Jiawei Han and Micheline Kamber. Elsevier, 2007
• 3 Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Education, 2007
• 4 Data Mining Concepts and Techniques by Jiawei Han and Micheline Kamber. Elsevier, 2007.

Reference Books

• 1 Introduction to Data Mining with Case Studies by G. K. Gupta. Prentice Hall of India, 2006.
• 2 Data Mining Methods and Models by Daniel T. Larose. Wiley-Interscience, 2006.
• 3 Insight into Data Mining Theory and Practice by K. P. Soman, Shyam Diwakar and V. Ajay. Prentice Hall of India, 2006
• 4 Data Warehousing, Data Mining and OLAP by Alex Berson and Stephen J. Smith. Tata McGraw–Hill Edition, 2007