Statistical Computing

Basic computing skills are essential for data manipulation and statistical simulations. Data manipulation is essential for exploring data, executing algorithms, and automation. Simulations can help verify mathematical proofs, test out the impact of violated assumptions, and validate your models to deepen your intuition.

Syllabi and Materials

Syllabi:

• 2102 Applied Statistical Computing
• 4206/5206 Statistical Computing and Introduction to Data Science

Pre-requisites

• Exposure to an introductory statistics class
  • Average, median, hypothesis testing, correlation, the difference between a random variables and its realization

Computer setting up

I encourage you to set up Jupyter Notebooks on your computer so you could repeat these in R or Python in the future.

Topic Notes

For people who purely want to learn data analysis with minimal amount of Computer Science background, I’ve written some notes to help:

• Learning R through Examples and Errors. After going through these notes, you should be able to
  • Collect data from the internet
  • Manipulate messy data into an easy-to-analyze format
  • Write custom functions to process the data
  • Summarize/plot data efficiently
• TODO, another set of notes that focuses on statistical computing
  • Empirically verifying theoretical ideas
  • Estimating probabilities using simulations
  • Bootstrap/cross validating etc
  • Basic lessons on distributed computing