Haiyuan Wang

Python pandas, numpy, sklearn; Machine learning: KNN, RFC, regression

Linear regression, multiple regression, t-test, F-test.

Nearly all the stuff that you will need as a data analyst. The professor is very efficient and knowledgable.

Exactly what the syllabus had said: simple and multiple regression, and diagnostics/remedial measures.

The math of linear regression models, diagnostics of linear regression models and model selection.

data modeling project, machine learning algorithms/application

Learned how to use functions in R to conduct different diagnostics and remedies to improve linear regression models.

Python from scratch to machine learning with scikit-learn. A lot of interesting topics including Regression, SVM, Decision Tress, Random Forest, Clustering, NLP and so on.

I learned a lot about linear regression: the different types of models, how to estimate coefficients, mean responses, and predict mean responses. In addition, I learned about different diagnostics used in linear regression, model selection processes, and remedial measures.

Very forms of regression from simple ols to ridge and lasso