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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)
2 Getting groovy with Git and GitHub
3 More Git, as well as mark-up languages, webpage basics and GitHub Pages
4 Introduction to RStudio and RMarkdown
5 Using Jupyter notebooks and RMarkdown to create professional and reproducible presentations
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
8 Reading the docs & getting help

Attributions

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

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.

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