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

A Data Science Revolution In Healthcare (M-I-T)

A Data-Driven Future For Medicine (M-I-T)

Adequate Representation and Justification of Data (M-I-T)

An example in R with MIMIC-III (M-I-T)

Best-Practices to Ensure Reproducibility (M-I-T)

Case Study: Reproducing Studies Using MIMIC-III (M-I-T)

Closing Remarks (M-I-T)

Intervention/Exposure (M-I-T)

Closing Summary and Next Steps for Independent Learning (M-I-T)

Introduction (M-I-T)

Collaborations and Data Source Bias (M-I-T)

Introduction to Regression Analysis: Part I (M-I-T)

Conclusion (M-I-T)

Introduction to Regression Analysis: Part II (M-I-T)

Considerations Of Fairness (M-I-T)

Iris Dataset and Decision Tree Introduction (M-I-T)

Control Group (M-I-T)

Linear Regression: Reinforcement Material (optional) (M-I-T)

Deep Learning: A Machine Learning Technique (M-I-T)

Logistic Regression: Contingency Tables (M-I-T)

Developing A Shared Language (M-I-T)

Reproducibility in Critical Care – Theoretical Concepts (M-I-T)

Logistic Regression: Hypothesis Testing (M-I-T)

EDA Tools (M-I-T)

Sensitivity Analysis (M-I-T)