WWU Data Science Seminar

Spring 2016

(Most) Tuesdays at 4pm in the Astro Lab

I am running a seminar this quarter on computational techniques for physics & astronomy undergraduates. This will be a series of short lectures and labs that demonstrate techniques, with optional homework “assignments”.

The goal of this course is not to make you data scientists. Nor is it to make you highly skilled astronomers. Instead I hope to give you a broad overview of the content that exists, so that you are empowered to learn more! Each lesson will use actual modern astronomical data for teaching.

Planned Lessons

Each lesson will point to a GitHub repository with notes, code, example data, etc. Students should submit their “homework” as Pull Requests.

1. Tools of the trade
2. Algorithm 1: Do something!
3. Algorithm 2: Try to break your code
4. Data wrangling, file Input/Output
5. Data visualization, what makes a “good” plot
6. Fitting a model to data, making measurements
7. Intro to machine learning

Links for class

• Anaconda, the awesome bundled Python install. required
• GitHub, you will need an account here
  • you can install the GitHub desktop client if you don’t have git on your computer
  • Notes from Stephanie Douglas’ lecture on Git and GitHub
  • Really Quick Git by Jim Vallandingham, quick tips on setting up and using Git/GitHub
• WWU Computer Science Fraser Lecture Series: The Big Data Revolution in Human and Environmental Health

The development of this seminar is supported by the National Science Foundation