A helpful 4-page data science 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 (some more in-depth than others) include:
- 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
- and more!
This cheatsheet will be occasionally updated with new/improved info, so consider a follow/star to stay up to date.
Current planned additions:
- Time Series (ARIMA, SARIMA)
- Additional statistical theories often seen in interviews
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.
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
Images are used for educational purposes, created by me, or borrowed from my colleagues here
Feel free to suggest comments, updates, and potential improvements!
Aaron Wang: Reach out via LinkedIn