/Data-Science-Cheatsheet

A helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between.

Primary LanguageTeX

Data Science Cheatsheet 2.0

A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between. This resource is not meant to be a comprehensive deep dive into any specific model, but rather a quick refresher on a few of the most fundamental machine learning algorithms. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this cheatsheet helpful as well.

Inspired by Maverick's Data Science Cheatsheet (hence the 2.0 in the name), located here.

Topics covered:

  • Linear and Logistic Regression
  • Decision Trees and Random Forest
  • SVM
  • K-Nearest Neighbors
  • Clustering
  • Boosting
  • Dimension Reduction (PCA, LDA, Factor Analysis)
  • Natural Language Processing
  • Neural Networks
  • Recommender Systems
  • Reinforcement Learning
  • Anomaly Detection
  • Time Series
  • A/B Testing

This cheatsheet will be occasionally updated with new/improved info, so consider a follow/star to stay up to date.

Future additions (ideas welcome):

  • Time Series Added!
  • Statistics and Probability Added!
  • Data Imputation

Links

Screenshots

Here are screenshots of a couple pages - the link to the full cheatsheet is above!

Why is Python/SQL not covered in this cheatsheet?

I planned for this resource to cover mainly algorithms, models, and concepts, as these rarely change and are common throughout industries. Technical languages and data structures often vary by job function, and refreshing these skills may make more sense on keyboard than on paper.

License

Feel free to share this resource in classes, review sessions, or to anyone who might find it helpful :)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Creative Commons License

Images are used for educational purposes, created by me, or borrowed from my colleagues here

Contact

Feel free to suggest comments, updates, and potential improvements!

Author - Aaron Wang