Beginner's Guide to Computation in Statistical Research

While the foundations of statistical methods are grounded in mathematical theory, we often need to demonstrate that these methods are useful in practice. This can done via toy examples, simulation studies, and applications to real-world datasets. We do not recommend spending hundreds of hours running calculations on datasets by hand. Thus, we will introduce the reader to core concepts of statistical computing and relevant resources in the CMU Department of Statistics and Data Science.

Implementation

The main aim for statistical computing is to implement proposed algorithms in code such that they can be applied to a variety of inputs very quickly. These implementations can be as simple as computing the OLS coefficients to optimizing a billion-parameter neural network.

These implementations are usually achieved through computer programming. As a student in the Statistics department, you will be exposed to a variety of programming languages and tools. Below is a reading list of first steps to get you started on your computation journey

1. Choosing and Installing a Programming Language
2. Installing and Configuring an Integrated Development Environment
3. Other Helpful Resources for Statistical Computing

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.

1. Parallelization: Making the most of your Machine
2. Building Workflows on Remote Servers

Reproducibility

Yes

1. GitHub
2. Virtual Environments
3. Docker

Collaboration

Topics like

• Developing python packages and Developing R packages