Data Mining is a course that introduces students to the concepts, principles, techniques, and applications of discovering meaningful patterns, relationships, trends, and knowledge from large and complex datasets. The course covers the data mining process, data preprocessing, data exploration, pattern discovery, classification, clustering, association rule mining, anomaly detection, prediction, and evaluation of data mining models.
Students will gain practical experience in preparing datasets, selecting appropriate data mining techniques, using data mining tools and programming environments, interpreting results, and translating discovered patterns into useful information for decision-making. Emphasis is placed on the application of data mining in real-world contexts, including business, education, government, information systems, libraries and information centers, research, and other technology-driven environments. Ethical considerations, data privacy, responsible use of data, and the limitations of data mining techniques are also discussed.
Prerequisite: Database Systems / Statistics or equivalent foundational course.