syllabus.qmd

---
title: "Overview"
---

## About the Computational Skills Workshop

The workshop will focus on skills related to computation, code development, and statistics/data science workflows, including,

 - open science workflows and literate programming;
 - introduction to version control, Git and GitHub;
 - code style;
 - debugging;
 - testing;
 - collaboration with Git and GitHub;
 - numerical analysis (random number generation and floating point precision);
 - packaging and reproducible environments; and
 - automated workflows.

### Goals

- Goal 1: Emphasize good computational and code development practices (scripting, version control, testing, modularity, defensive programming, documentation, commenting, numerical analysis issues).
- Goal 2: Emphasize good practices for workflows, including reproducibility, automation, isolated environments.
- Goal 3: Provide practice with and introduce key tools for version control, testing, documentation, literate programming (documents with runnable code).


## (Optional) Introduction to Computing and Python

The optional additional sessions (held Tuesday-Wednesday, August 20-21) provide a basic introduction to computing concepts (e.g., parts of a computer, ideas related to parallelization, introduction to the the shell/command line/terminal) and an introduction to Python.

These are intended for those with little experience (or wishing a refresher) with working in a command line context or with using Python, and are particularly important for those taking Statistics 243, which assumes basic knowledge of Python.

The introduction to Python will borrow heavily from [this Software Carpentry Python lesson](https://swcarpentry.github.io/python-novice-inflammation/), with some additional topics added.