Section 1: Storing and Accessing Data1. The Healthcare IT Landscape2. Relational Databases3. SQL
4. Example Project 1: Querying Data with SQL5. Non-Relational Databases6. M/MUMPS
Section 2: Understanding Healthcare Data7. How to Approach Healthcare Data Questions8. Clinical and Administrative Workflows: Encounters, Laboratory Testing, Clinical Notes, and Billing9. HL-7 and FHIR, and Clinical Document Architecture10. Ontologies, Terminology Mappings and Code Sets
Section 3: Analyzing Data11. A Selective Introduction to Python and Key Concepts12. Packages, Interactive Computing, and Analytical Documents13. Assessing Data Quality, Attributes, and Structure14. Introduction to Machine Learning: Regression, Classification, and Important Concepts15. Introduction to Machine Learning: Support Vector Machines, Tree-Based Models, Clustering, and Explainability16. Computational Phenotyping, and Clinical Natural Language Processing17. Example Project 2: Assessing and Modeling Data
18. Introduction to Deep Learning and Artificial Intelligence
Section 4: Designing Data Applications19. Analysis Best Practices20. Overview of Big Data Tools: Hadoop, Spark and Kafka21. Cloud Technologies