Statistics cheat sheet for data scientists pdf

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.

Table of Contents Show

Screenshots
Why is Python/SQL not covered in this cheatsheet?
Table of Contents
Supervised Learning
Unsupervised Learning
Hacks, tricks and tips
Chossing the right model
Neural Nets
What is data science cheat sheet?
What are the 4 basic elements of statistics?
What does μ0 mean?
What are the most important topics in statistics?

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.

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

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What is data science cheat sheet?

What are the 4 basic elements of statistics?

Sample size, variables required, numerical summary tools, and conclusions are the four elements of a descriptive statistics problem.

What does μ0 mean?

One-Sample. Tests whether the mean of a normally distributed population is different from a specified value. Null Hypothesis (H0): states that the population mean is equal to some value (μ0)

What are the most important topics in statistics?

Statistics Department Common discrete and continuous distributions. Bivariate distributions. Conditional probability. Random variables, expectation, variance.