Presents a comprehensive survey of the different types of datasets encountered in health AI and machine learning applications. Students work with major categories of health data─from population surveillance and wearable sensors to clinical records and genomics─to gain hands-on experience. For each dataset type, explores data provenance, ethical considerations, regulatory requirements, data quality challenges, and appropriate analytical approaches. Offers students an opportunity to develop practical skills in data cleaning, preprocessing, and applying machine learning methods to real-world health datasets through guided assignments using the data. Emphasizes critical evaluation of data limitations, bias, and fairness considerations specific to each data modality.