Discussion:

• Wednesday 7:30-8:45 pm, online

• Friday 2:30-3:45 pm, 105 Brogden Psychology Building

Office hours: Tueday & Thursday 7:00-8:00 pm, CST, online

Tentative topics:

• Course projects: Module 1, 2, 3

• Project related skills: RShiny, Jupyter Notebook, Github, JSON, etc.

• The Unix Shell: CLI commands, pipes and filters

• Optimization: gradient descent, SGD, Newton’s method

• dplyr

• SQLite

• Data Science interview questions

Hardware Requirements:

Students need to have a PC/laptop running either Mac or Linux. If your computer has only Windows installed, I recommend the following two options:

• Run a virtual linux computer (Easy)

• Install Linux in dual boot with Windows (Requires some effort but it is interesting)

Personally, I like the following Linux distros: Manjaro, Linux Mint, Pop!_OS

Resources:

• Karl Broman’s tutorials on git/github, make, perl, and more
• Cécile Ané’s course (STAT 679/992) on Computing Tools for Data Analytics
• CS231n, CS246, CS229T from Stanford
• R for Data Science
• Editor/IDE: VS Code, PyCharm, Sublime, SpaceVim
• Google Colab
• Remzi Arpaci-Dusseau’s free book on Operating Systems (CS 537)