Beginner's Guide to Computation in Statistical Research

At some point, you will need to demonstrate that your statistical methods actually work in practice. Whether you are running a toy simulation, analyzing real-world data, or optimizing a complex neural network, you will need to write code. If you are new to computing, the wiki can be overwhelming. Read every section top to bottom up through the Reproducibility and Collaboration section. (You do not need to read every page in every section, but note their existence) It is designed to take you from installing your first language to running massive workflows on department servers.

Implementation

Your first goal is to translate mathematical algorithms into functional code. It is important to familiarize yourself with these concepts early on.

Choosing and Installing a Programming Language
Help deciding between R, Python, Julia, or other languages and how to get them running on your personal machine.

Installing and Configuring an Integrated Development Environment
Your IDE (Integrated Development Environment) is your workshop. Learn how to set up VS Code or RStudio.

Beyond Print Statements: Breakpoint Debugging
Your IDE (Integrated Development Environment) is your workshop. Learn how to set up VS Code or RStudio.

Command Line Basics
Essential survival skills for navigating your computer without graphical windows

Scale and Efficiency

Once you have gotten the hang of implementing statistical algorithms in code, the next major hurdle will be making your workflows scalable and efficient. For code running on your own machine, you want to make sure you are using every CPU core and every bit of RAM as efficiently as possible. Even then, your personal computer alone can only get you so far. Thankfully, the department also provides several options for remote high-performance computing for even the largest of research workflows.

Building Workflows on Remote Servers
Using the department servers and other computing clusters

Parallelization: Making the most of your Machine
How to run multiple experiments simultaneously without babysitting your machine

Big Data Management
How to handle datasets that are measured in terabytes

Reproducibility and Collaboration

While it is important that you are able to obtain results efficiently, it is also equally important for other members of the academic community to arrive at your same results independently. Specifically, others should be able to run your workflow and obtain the exact same output as you did with minimal tinkering and friction.

Version Control
Keeping track of code/experiment history

Dependency Management
Making sure anyone else in the world can run your workflow

Software Development (Optional)

If you envision yourself making your methods and associated algorithms available for mass adoption, there are other considerations for publishing your code that go beyond simple reproduciblity. Other should not only be able to reproduce your results, but also modify your code for new experiments with minimal hassle.

Creating Libraries and Packages
How to structure your code to for easy consumption by other researchers

Working with package repositories (TBW)
How to upload your packages to public repositories for easy widespread distribution.