MIT MM Statistics and Data Science cheatsheets

These are my cheatsheets for the capstone exams in the MIT MicroMaster in Statistics and Data Science. Note that they are not exhaustive; these are what I thought would be most helpful to me on an exam. (The Part 2 cheatsheet does not cover neural networks just because I ran out of time). Feel free to use them for your own studies or as a template for your own cheatsheets.

Part 1: Probability and Statistics Theory

This cheatsheet contains materials from the following courses:

• 6.431: "Probability – The Science of Uncertainty and Data"
• 18.6501: "Fundamentals of Statistics"

Part 2: Statistics and Machine Learning

This cheatsheet contains materials from the following courses:

• 6.419: "Data Analysis: Statistical Modeling and Computation in Applications"
• 6.86: "Machine Learning with Python: From Linear Models to Deep Learning"
• 18.6501: "Fundamentals of Statistics"

Compilation

The documents are meant to be built by latexmk and LuaTeX. All the class and package requirements should come in a usual TeX Live installation. You could compile them yourself:

cd tex
lualatex -synctex=1 -interaction=nonstopmode -file-line-error -pdf part_1.tex
lualatex -synctex=1 -interaction=nonstopmode -file-line-error -pdf part_2.tex

Or use the provided Makefile:

make part_1.pdf
make part_2.pdf

Credits

All credits for the course material go to the course instructors:

• 6.431: John Tsitsiklis, Patrick Jaillet, Dimitri Bertsekas
• 18.6501: Philippe Rigollet
• 6.86: Regina Barzilay, Tommi Jaakkola
• 6.419: Caroline Uhler, Stefanie Jegelka

Additional credit goes to all the TAs, whose recitations were a huge help. Any mistakes are my own.