This course introduces the characteristics of medical data and associated data mining challenges on dealing with such data. It focuses on studying those data science techniques in the context of concrete healthcare analytic applications such as predictive modelling, computational phenotyping and patient similarity, diseases detection. In this course, the students will learn how can the application of data analytics improve health and health care and how the data analytics-based solutions can result in better diagnosis, and better care. In healthcare, large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. The basics of data mining within the context of a wide variety of health care settings, and the types of data and data analysis challenges that you will likely encounter by gathering the data, move on to classifying, analyzing and finally visualizing medical data. The course will host a number of experts in the field of IT operations and related healthcare sector from the local market to cover the practical side of the course and share their first-hand experience with students.