“Data science” is a big term; however, we still try to capture all of the topics, hoping to be a lighthouse which points the way you need.

It covers the clarification of confusing terminology, correlation analysis, principal component analysis (PCA), hypothesis testing, ordinary least squares (OLS), logistics regression, pandas, support vector machine (SVM), the tree methods (random forest and gradient boosted decision trees), KNN for recommendation, k-means for clustering, cross validation, pipelining, and more.

And the most important thing: all are introduced in plain Python!

The notebooks are available on https://github.com/moskytw/data-science-with-python .