Coursera – Measuring Causal Effects in the Social Sciences (University of Copenhagen)
- Modules: The nature of causal effects and how to measure them, The multivariate regression model and mediating factors, Randomized controlled trails, Bloom eauation, Hawthorne effect, and instrumental variables, Difference in Difference.
Coursera – Practical Machine Learning (Johns Hopkins University, Bloomberg School of Public Health) GitHub-repo
- Modules: Cross validation, Caret package, Principle components analysis, Predicting with trees, Bagging, Random forests, Boosting, Model based prediction, Regularized regression, Combining predictors.
MITx-14.310x – Data Analysis for Social Sciences (taught by Esther Duflo et al.)
- Modules: Causality, Analyzing randomized experiments, Intro to machine learning, Data visualization, DID, RD design, Endogeneity and instrumental variables, Experimental design.
MITprofessionalx – Data Science: Data to Insights (taught by Victor Chernozhukov et al.)
- Modules: Making sense of unstructured data, Regression and prediction, Classification, hypothesis testing and anomaly detection, Recommendation systems, Networks and graphical models.
Jauer 