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StanfordOnline: Statistical Learning with Python

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.

Statistical Learning with Python
11 weeks
3–5 hours per week
Progress at your own speed
Optional upgrade available

There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts Feb 22
Ends Aug 26

About this course

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This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing in this course is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures and computing are done using R.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023. The pdf for this book is available for free on the book website.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated skills:Basic Math, Logistic Regression, Statistical Learning Theory, Boosting, Support Vector Machine, Statistical Modeling, Supervised Learning, Deep Learning, Unsupervised Learning, Statistics, K-Means Clustering, Principal Component Analysis, Random Forest Algorithm, Bootstrap (Front-End Framework), Artificial Neural Networks, Lasso (Programming Language), Lecturing, Polynomial Regression, Linear Discriminant Analysis, Data Science, Python (Programming Language)

What you'll learn

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  • Overview of statistical learning
  • Linear regression
  • Classificaiton
  • Resampling methods
  • Linear model selection and regularization
  • Moving beyond linearity
  • Tree-based methods
  • Support vector machines
  • Deep learning
  • Survival modeling
  • Unsupervised learning
  • Multiple testing

Frequently Asked Questions

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Do I need to buy a textbook?

The book is nice to own, and is available from Amazon and other booksellers. However a free online version of An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribute printed versions of that pdf file.

Are Python and Jupyter Notebook available for free?

Yes. You get Python for free from Typically it installs with a click. You get Jupyter Notebook from, also for free, and a similarly easy install.

How many hours of effort are expected per week?

We anticipate it will take approximately 3-5 hours per week to go through the materials and exercises in each section.

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