Research Computing Services workshop materials for statsmodels and Scikit-learn.
You can download all of the files by clicking the green button above and choosing "Download ZIP."
If you download files from the links above, you have to click through to the RAW version of the notebook and download that. If you download directly from the links above, the files won't open because they are web pages, not the raw files.
To download just the exercise files, right-click on the links below, and choose Save Link As (or the similar option in your browser). Make sure to choose All file types as the content type (or .ipynb if available), and remove any .txt or similar extensions from the file when you save it. The files should be *.ipynb files, with no additional file type extensions.
See Resources for a listing of general Python resources, tutorials, and reference materials. Links below relate specifically to material covered in this workshop.
Introduction to Statistics with Python: link is to a GitHub repository for materials to accompany a book by the same name by Thomas Haslwanter. Examples focus on the life sciences. The book is available online through the Northwestern library (login required).
statsmodels Examples are in the project's GitHub repository, in addition to in the documentation
Model evaluation, model selection, and algorithm selection in machine learning: this is the first in a three part series of good explanations/tutorials for those looking to better understand how to navigate options in machine learning; written by Sebastian Raschka, a computational biologist (but the material is for a general audience)
Scikit-Learn Cheat Sheet: common models and steps
Machine Learning with Scikit-Learn: videos, notebooks, and Kaggle blog posts covering the basic models and ideas behind them