This course introduces fundamental concepts of information theory and their applications in modern data science and machine learning. Topics include entropy, mutual information, KL divergence, channel capacity, rate-distortion theory, and information-theoretic bounds. Students will learn how these principles connect to practical data science workflows, including feature selection, representation learning, compression, communication-efficient learning, and querying/processing large-scale datasets with SQL for information-theoretic analysis (e.g., aggregations, empirical distributions, entropy/MI estimation from relational data). The course emphasizes hands-on assignments and projects using real-world datasets, combining mathematical foundations with implementation skills.