aaronwangy/Data-Science-Cheatsheet

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


repo name	aaronwangy/Data-Science-Cheatsheet
repo link	https://github.com/aaronwangy/Data-Science-Cheatsheet
homepage
language	TeX
size (curr.)	5611 kB
stars (curr.)	3222
created	2021-02-05
license

Data Science Cheatsheet 2.0

A helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between. It covers over a semester of introductory machine learning, and is based on MIT’s Machine Learning courses 6.867 and 15.072. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this resource 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 or star to stay up to date.

Future additions (ideas welcome):

~~Time Series~~ Added!
~~Statistics and Probability~~ Added!
Data Imputation
Generative Adversarial Networks
Graph Neural Networks

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.