View source for Other Helpful Resources for Implementation

Back to [[Beginner's Guide to Computation in Statistical Research]]

== Productivity Tools ==
{| class="wikitable"
|+
!Title
!Description
!Technical Level
!Scope
|-
|[[ChatGPT and other LLMs]]
|Overview of how to use LLMs for computing tasks
|⭐⚫⚫⚫⚫
| style="background-color: #44ee88;" |All Computing Tasks
|-
|[[GitHub Copilot]]
| Instructions for installing Copilot for your IDE. Copilot is an AI-powered code generation and editing tool.
|⭐⚫⚫⚫⚫
| style="background-color: #44ee88;" | Any code written in an IDE
|-
|[[Amphetamine]]
|Tool for preventing your device from entering sleep mode while you are away
|⭐⚫⚫⚫⚫
| style="background-color: #eecc66;" |Personal devices
|}

==CMU Courses==
The CMU Department of Statistics and Data Science provides a number of courses that will walk through many of the basic steps of building research workflows using code

*[https://36-750.github.io/ 36-750], 36-708, and 36-707 are required PhD courses that cover implementations of common statistical algorithms in code.
*[https://www.cs.cmu.edu/~nihars/teaching/10715Fa25.html 10-715] in the Machine Learning Department introduces students to common ML algorithms and their implementation in code.
*Other courses pertaining to algorithms and software development can be taken depending on your specific research requirements and interest.

==StatBytes==


[https://drive.google.com/drive/folders/1W1fkOW73dUCMVX-U-6gjMRrSMLXf4OYd?usp=drive_link Past Presentations]

==External Resources==