README.md

DSCI 521: Computing Platforms for Data Science

How to install, maintain, and use the data scientific software "stack". The Unix operating system, integrated development environments, and problem solving strategies.

Course Learning Outcomes

By the end of the course, students are expected to:

1. Use the Unix command line to navigate their computer's filesystem.
2. Define and distinguish between absolute file paths and relative file paths.
3. Effectively use local and remote version control software (e.g., Git and GitHub) to organize projects and manage file versions.
4. Create, edit and run reproducible literate Python and R code documents (e.g., reports and presentations) using Jupyter and RMarkdown.
5. Write and edit Markdown and in-line LaTeX syntax within literate code documents.
6. Define and correctly use a project working directory.
7. Diagnose and troubleshoot programming and development environment problems, and explain how such problems can be avoided.

Assessments

This is an assignment-based course. You'll be evaluated as follows:

Assessment	Weight	Location
MDS Policies quiz	2%	At home, on Canvas
Lab 1 - Introduction to MDS homework and practice with Jupyter Notebooks	13%	Submit to Github
Lab 2 - Creating webpages/blogs with Git and GitHub	15%	Submit to Github
Lab 3 - Introduction to RStudio and RMarkdown for reports and presentations	15%	Submit to Github
Lab 4 - RStudio projects and driving Git from RStudio	15%	Submit to Github
Quiz 1	20%	On Canvas and written in your lab room
Quiz 2	20%	On Canvas and written in your lab room

Tip: Use the lecture learning objectives as beacons when studying for your quizzes!

Course preparation

Follow the installation guide for the UBC Master of Data Science Software Stack here: https://ubc-mds.github.io/resources_pages/installation_instructions/

Lectures

Lecture	Topic	Readings
1	Introduction to MDS tools (Unix, Git/GitHub, Jupyter)	The Unix Shell: Navigating Files and Directories or The shell Introduce yourself to Git Connect to GitHub Jupyter Notebook Tutorial: The Definitive Guide
2	Getting groovy with Git and GitHub	Excuse me, do you have a moment to talk about version control? by Jenny Bryan Comparing commits across time Resolving a merge conflict using the command line nbdime – diffing and merging of Jupyter Notebooks
3	More Git, as well as mark-up languages, webpage basics and GitHub Pages	.gitignore Fork a repo Set up keys for SSH for use with GitHub Getting Started with GitHub Pages Markdown cheat sheet HTML cheat sheet (reference) LaTeX cheat sheet (reference)
4	Introduction to RStudio and RMarkdown	RStudio's panes R Markdown Cheat Sheet (reference) R Markdown Reference Guide (reference) R Markdown lessons from RStudio R Markdown: The Definitive Guide (reference) R Markdown code chunk options (reference)
5	Using Jupyter notebooks and RMarkdown to create professional and reproducible presentations	R Markdown presentations with ioslides R Markdown presentations with xaringan Jupyter presentations with RISE
6	Let's talk about filenames and Data Science project organization	Good Enough Practices in Scientific Computing
7	RStudio projects and driving Git from RStudio	Using RStudio Projects Connect RStudio to Git and GitHub Workflow: projects
8	Reading the docs & getting help	Three tips for posting good questions to R-help and Stack Overflow How to create a Minimal, Complete, and Verifiable example

Attributions

Materials were inspired, re-used and re-mixed from the following sources:

• Software Carpentry, specifically the Unix Shell and Git lessons
• Happy Git and GitHub for the useR by Jenny Bryan and the STAT 545 TAs
• Data 8: The Foundations of Data Science, specifically Lab 01
• Data Carpentry Reproducible Science Workshop

License

The UBC Master of Data Science DSCI 521: Computing Platforms for Data Science course materials here are licensed under the Creative Commons Attribution 2.5 Canada License (CC BY 2.5 CA). If re-using/re-mixing please provide attribution and link to this webpage.