Collaborative Data Science for Healthcare (Fall 2020) (M-I-T)

• Introduction (M-I-T)
• What is Data Analysis? (M-I-T)
• Developing A Shared Language (M-I-T)
• Adequate Representation and Justification of Data (M-I-T)
• Get To Know Your Data (M-I-T)
• Valid Ranges of Features (M-I-T)
• Evaluating Analyses (Example) (M-I-T)
• Summary (M-I-T)
• A Data Science Revolution In Healthcare (M-I-T)
• Technologies Driving Healthcare AI (M-I-T)
• Utilizing Healthcare Data (M-I-T)
• Machine Intelligence (M-I-T)
• Deep Learning: A Machine Learning Technique (M-I-T)
• A Data-Driven Future For Medicine (M-I-T)
• Machine Learning Powered Medical Diagnostics: An Example (M-I-T)
• Medicine Rebuilt As Data Science (M-I-T)
• The Prerequisite for High-Quality Data Analytics (M-I-T)
• What Is Hypothesis Testing (M-I-T)
• Multiple Testing And Spurious Findings (Example: Dead Salmon Study) (M-I-T)
• Reproducibility Crisis (M-I-T)
• Reasons for Concern (M-I-T)
• Considerations Of Fairness (M-I-T)
• Randomized Controlled Trials (M-I-T)
• FINER Mnemonic (M-I-T)
• The Clinical Scenario: Impact of Indwelling Arterial Catheters (M-I-T)