We target students who are interested in how medical care can be transformed by “Big data”. We use real-life clinical use cases to motivate how clinical care can be enhanced by collecting unconventional data of various kinds and feeding such data to machine learning methods to build next-generation clinical tools. However, even with the appropriate data collected, we describe the data science challenges that need to be overcome before the data can be used properly. We give an introduction to the main concepts and methods in biostatistics, and machine learning, including unsupervised and supervised methods, and data visualization. Last but not least, we discuss ethical, and fairness issues and other potential pitfalls of applying machine learning to medical care. 

The four desired outcomes of the package are (i) examples of clinical use cases which would benefit from data science approaches; (ii) concepts and methods in handling data of different kinds and in machine learning; (iii) hands-on experience in using those methods; and (iv) ethical and societal considerations when deploying such approaches in medical care.