Business Intelligence and Data Science (BIT422CO)
Foundations of business intelligence, data warehousing, visual analytics with Tableau/Power BI, WEKA data mining, text/web analytics, and big data architectures.
Unit 1: Overview of Business Intelligence & Decision Support6 hrs
- Decision support systems (DSS) concept and evolution
- Business intelligence definitions, frameworks, and architecture
- OLTP vs OLAP transaction vs analytical processing models
- Business analytics overview: descriptive, predictive, prescriptive
Unit 2: Data Warehousing and ETL Processes6 hrs
- Data warehousing definitions, characteristics, and architectures
- Multidimensional data modeling: Star, Snowflake, and Fact Constellation schemas
- Data extraction, transformation, and loading (ETL) pipeline architecture
- Real-time data warehousing and operational data stores (ODS)
Unit 3: Business Reporting & Visual Analytics6 hrs
- Business reporting definitions, metrics, and report types
- Information visualization and visual analytics principles
- Performance dashboards, scorecards, and Balanced Scorecard methodology
- Visual analytics tools: Tableau, Microsoft Power BI, and interactive dashboards
Unit 4: Data Mining Concepts & Applications9 hrs
- Data mining definitions, process models (CRISP-DM), and taxonomy
- Association rule mining: Apriori algorithm and FP-Growth
- Classification and clustering methods in data mining
- Data mining software and open source toolkits: WEKA, RapidMiner
Unit 5: Text and Web Analytics6 hrs
- Text analytics and Natural Language Processing for business insights
- Sentiment analysis and opinion mining architectures
- Web mining taxonomy: Web content mining, Web structure mining, and Web usage mining
- Social media analytics and network metrics
Unit 6: Big Data Analytics & Stream Processing5 hrs
- Big Data definition, V-characteristics (Volume, Velocity, Variety, Veracity, Value)
- Hadoop ecosystem and MapReduce processing framework
- NoSQL database architectures for analytical workloads
- Real-time stream analytics and in-memory computing architectures
Unit 7: Business Analytics Emerging Trends and Ethics7 hrs
- Location-based analytics and geospatial business intelligence
- Automated decision making and recommendation systems
- Data governance, privacy laws (GDPR), and ethical considerations in analytics
- Cloud-based BI and analytics service models
Reference books
- Sharda, Ramesh, Dursun Delen, and Efraim Turban, Business Intelligence, Analytics, and Data Science: A Managerial Perspective, Pearson.
- Inmon, W. H., Building the Data Warehouse, John Wiley & Sons.
- Han, Jiawei, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, Morgan Kaufmann.
- Kimball, Ralph and Margy Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Wiley.