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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.
