You may complete any of the below for one point of extra credit each (5 points maximum).

Course feedback

Over 80 people have contributed to R for Data Science. Your feedback will make this course better for you and future students.

  • give five pieces of feedback/corrections to course material
    • typos/spelling/grammar (there are likely many)
    • rewording awkward/unclear language
    • improve visualizations
    • point out explanations you found effective/could be better
  • post each piece of feedback as a github issue on our repo https://github.com/idc9/stor390/issues

Before you post a github issue you must sign up for github and send me your username. I suggest publicly associating your github profile with yourself, however, if you are strongly inclined otherwise you may sign up anonymously.

Once you have posted 5 issues send me the links to the issues and I will mark this down.

Attend an event

Attend an event related to data science and send me a quick blurb about what you learned. I’ll post some events below, but you are welcome to find other ones (as long as you check with me first)

Publish a blog post

Write a blog post about something related to data science and post is somewhere. You can post it to my blog (or your own if you have one). However I encourage you to reach out to existing data science related to blogs (you lose nothing by sending FiveThirtyEight an email).

I’m using a loose definition of a blog post, for example a coding project like one of Google’s A.I. experiments counts as a blog post. You are welcome to use a project from another class/undergrad research as long as you add something on top of what you have already done.

If you want do some an ambitious project I’m open to counting it for more than one point – but don’t go overboard. See me before you built a self driving car in your garage.

Here are some blogs to use as inspiration