Tribhuvan University
Faculty of Management
Office of the Dean
Bachelor of Information Management (BIM)
Course Objective
:This course aims to enable students to understand the role and importance of Business Intelligence (BI) in modern organizations, learning key concepts and techniques in data warehousing, data mining, and data visualization. They will gain hands-on experience with popular BI tools and technologies while developing the skills needed to analyze and interpret data to support effective decision-making processes.
Course Description
:This course introduces the concepts, techniques, and tools used in business intelligence (BI) and data analytics. Students will learn how BI is used to transform data into insights for decision- making in organizations.
Course Contents:
Lecture hours show the approximate classroom time allocated to each unit.
Unit 1.Introduction to Business Intelligence
- Definition and scope of BI
- Importance of BI in decision-making
- Overview of BI tools and technologies.
Unit 2.Data Warehousing
- Introduction to data warehousing concepts
- Data warehouse architecture
- ETL (Extract, Transform, Load) processes
- Data modeling for BI.
Unit 3.Data Mining
- Introduction to data mining concepts
- Data preprocessing techniques
- Classification and clustering algorithms
- Association rule mining.
Unit 4.Data Visualization
- Principles of data visualization
- Tools and techniques for data visualization
- Dashboard design and best practices
Unit 5.BI Tools and Technologies
- Overview of popular BI tools (e.g., Tableau, Power BI)
- Hands-on experience with BI tools for data analysis and reporting
- Introduction to OLAP (Online Analytical Processing)
Unit 6.BI Implementation
- Best practices for implementing BI solutions
- Challenges and considerations in BI implementation
- Case studies of successful BI implementations Laboratory Work The laboratory work involves learning to use various Business Intelligence (BI) tools and technologies. It also includes implementing data warehousing, data mining, and data visualization techniques.
Suggested Readings:
Sherman, R. (2014). Business intelligence guidebook: From data integration to analytics. Morgan Kaufmann. Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media. Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. Wiley.