There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT‐Scan, X‐ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above‐mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them. The aim of this course is to introduce students to the complexities of biomedical big data. A data scientist is typically removed from the data generating process and involved further downstream during the data analysis phase. However, a thorough and meaningful analysis of such data cannot be performed without an in‐depth understanding of how data was generated. Therefore, the students will learn about the nuances and intricacies of data generated in four typical sources of big data in medical studies, namely, 1) big administrative database for healthcare, 2) biomedical imaging, 3) genomics and 4) sensors and wearable devices.