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.
The main way this goal is achieved is through the using of 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
- Choosing and Installing a Programming Language
- Installing and Configuring an Integrated Development Environment
