CommunityData:StatsGaps

DRAFT -- IN PROGRESS -- IGNORE

Welcome to the StatsGaps StudyGroup page -- a set of suggested learning pathways making use of course resources produced by community data faculty, meant to be used by folks who have a mixture of familiarity and non-familiarity with R, statistics, and research processes. The primary text is: [Open Intro to Statistics]

We borrow heavily from the course most recently taught by Aaron: [[1]]

Follow the strand(s) that apply to you:

• Learn R -- you don't know R
• Learn Stats -- you haven't taken much if any statistics, or otherwise feel you're mostly starting from scratch
• Refresh -- overview and shore up your stats knowledge if it feels rusty
• Stronger -- your stats knowledge is strong but your class stopped before you got to good stuff you see used in lots of the papers in this group, like regression)

Week 1

All:

• Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks. Proceedings of the National Academy of Sciences 111(24):8788–90. [Open Access] If you haven't read this, you should--also, check the 'Editorial expression of concern' at the top and toss "kramer guillory hancock 2014" if you want to see the firestorm that this article created.-khc

Learn R:

• Week 1 R lecture materials (.zip file)
• Week 1 screencast (part 1, 23 minutes) (the video should load directly in browser window)
• Week 1 screencast (part 2, 27 minutes)

Learn Stats:

• Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data)
• [Do Problem Set 1]

Refresh:

• Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data)
• [Read Problem Set 1]

Stronger:

• [Skim Problem Set 1] -- since we may discuss it f2f. Take a look at the text's Chapter 1 if you find any of the questions to be confusing or the answer you came up with is different than the key.

Extra Resources:

• Mine Çetinkaya-Rundel's OpenIntro §1 Lecture Notes
• OpenIntro Video Lectures including some for §1

Week 2: Probability and Visualization

All:

• Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. American Behavioral Scientist. 56(4): 459-487. [[2]]

Learn R:

• Week 2 R lecture materials (.Rmd file)
• Week 2 screencast (17 minutes)

Learn Stats:

• Diez, Barr, and Çetinkaya-Rundel: §2 (Probability)
• Do problem set 2 -- Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

Refresh:

• Diez, Barr, and Çetinkaya-Rundel: §2 (Probability)
• Read problem set 2 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

Stronger:

• Skim problem set 2 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

Extra Resources:

• Seeing Theory §1 (Basic Probability) and §2 (Compound Probability). (Note: this site provides a beautiful visual introduction to core concepts in probability and statistics).
• Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in Proceedings of the 8th ACM Conference on Designing Interactive Systems. Aarhus, Denmark: ACM. [PDF]
• Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes
• Video Lectures including 2 short videos for §2

Week 3: Distributions

All: (N/A)

Learn R:

• Week 3 R lecture materials (.Rmd file)
• Week 3 screencast (19 minutes)

Learn Stats:

• Read Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4 (Aaron says: You should read the rest of the chapter (§3.3 and §3.5). I won't assign problem set questions about it but it's still important to be familiar with.)
• Do Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3

Refresh:

• Read Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3

Stronger:

• Skim Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3

Extra Resources:

• Seeing Theory §3 (Probability Distributions).
• Mine Çetinkaya-Rundel's OpenIntro §3 Lecture Notes
• OpenIntro Video Lectures including 2 videos for §3.1 and §3.2