This course introduces the characteristics of medical data and associated data mining challenges on dealing with such data. It covers various algorithms and systems for big data analytics. It focuses on studying those big data techniques in the context of concrete healthcare analytic applications such as predictive modelling, computational phenotyping and patient similarity. In this course, the students will learn how can the application of data analytics to big data actually improve health and health care. The course shows that novel 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.