The course Data Sciences: Data Warehousing and Data Mining offers a comprehensive introduction to the theory and practice of data warehousing and data mining, which are essential components of modern data-driven decision-making. Designed for students with foundational knowledge in Python programming, databases, and computer systems, this 4-credit course covers key concepts such as data preprocessing, data warehouse modelling, OLAP operations, frequent pattern mining, association rules, classification techniques, clustering, and outlier detection. Students will gain hands-on exposure to data mining methodologies, understand their integration with databases and data warehouses, and explore applications such as web mining. By the end of the course, learners will be able to explain data warehousing architectures, design schemas, perform data cube computations, apply preprocessing and cleaning techniques, implement association rule mining, classification, clustering, and outlier detection, and interpret results for practical and research applications.
| Course Status : | Upcoming |
| Course Type : | |
| Language for course content : | English |
| Duration : | 12 weeks |
| Category : |
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| Credit Points : | 4 |
| Level : | Undergraduate/Postgraduate |
| Start Date : | 26 Jan 2026 |
| End Date : | 30 Apr 2026 |
| Enrollment Ends : | 28 Feb 2026 |
| Exam Date : | |
| Translation Languages : | English |
| NCrF Level : | 4.5 — 5.5 |
| Industry Details : | Education and Training |
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swayam@nitttrc.edu.in, swayam@nitttrc.ac.in
Week 1: Introduction to Data Mining.
Week 2: Data Preprocessing.
Week 3: Introduction to Data Warehousing.
Week 4: Data Warehouse Modelling.
Week 5: OLAP and Data Cube Computation.
Week 6: Frequent Pattern Mining.
Week 7: Association Rule Mining.
Week 8: Classification Basics.
Week 9: Advanced Classification.
Week 10: Cluster Analysis.
Week 11: Outlier Detection.
Week 12: Web Mining and Applications.
1. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, Elsevier
2. Margaret H Dunham, Data Mining Introductory and Advanced Topics, Pearson Education
3. Amitesh Sinha, Data Warehousing, Thomson Learning, India.
4. Xingdong Wu, Vipin Kumar, the Top Ten Algorithms in Data Mining, CRC Press, UK.
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