Thread by @grant_mcdermott

I'm teaching a "data science for economists" course this semester.

If you're interested in learning more about #rstats, Git(Hub), programming, databases, cloud computation, ML, etc., I'll be making all of my course material publicly available here: github.com/uo-ec607

As I say in the syllabus, this course basically covers all of the things I wish I'd been taught in grad school. At the same time, I've benefited immensely from so many people making their teaching materials (and software!) publicly available. This is me trying to pay it forward.

Here's a short-cut to the lecture slides and notes: github.com/uo-ec607/lectu…

Lecture 1: Course motivation and introduction to data viz (with ggplot2). raw.githack.com/uo-ec607/lectu…

Lecture 2: Version control with Git(Hub). raw.githack.com/uo-ec607/lectu…

Lecture 3: Learning to love the shell. raw.githack.com/uo-ec607/lectu…

(Also, it turns out that posting screenshots of my slides is an excellent way to spot typos after the fact.)

Lecture 4: R language basics. raw.githack.com/uo-ec607/lectu…
#rstats

Lecture 5: Data work in the #tidyverse.

Slides: raw.githack.com/uo-ec607/lectu…

Notebook: raw.githack.com/uo-ec607/lectu…

Lecture 6: Webscraping: (1) Server-side and CSS. raw.githack.com/uo-ec607/lectu…

(With an application on scraping Men's 100m world record times from Wikipedia.)

Lecture 7: Webscraping: (2) Client-side and APIs. raw.githack.com/uo-ec607/lectu…

Including an eccentric mix of applications from NYC trees... to US GNP data... to World Rugby rankings (most importantly obvs).

Lecture 8: Regression analysis in R. raw.githack.com/uo-ec607/lectu…

A whirlwind tour of (nearly!) all the main regression functions and packages that an applied economist could want: OLS, IV, FE models, robust and clustered SEs...

Lecture 9: Spatial analysis in R. raw.githack.com/uo-ec607/lectu… #rstats #spatial

Lecture 10: Writing functions and programming intro. raw.githack.com/uo-ec607/lectu… #rstats #programming

(No nice data visualizations for this lecture, so here is a picture of a border collie puppy instead. Also: objectively the best kind of puppy.)

Lecture 11: Advanced function and programming topics. raw.githack.com/uo-ec607/lectu…

Debugging tools, catching user errors... and caching results. (So that you don't have to live with the emotion of re-running everything when your code crashes.)

Lecture 12: Parallel programming. raw.githack.com/uo-ec607/lectu… #rstats #parallel #multicore

Learn how easy it is to run R code in parallel (and then congratulate yourself for being so awesome).

Lecture 13: Docker. ropenscilabs.github.io/r-docker-tutor…

(Took a break from preparing lecture notes for this class and just went through @rOpenSci's excellent tutorial. Highly recommended.)

(University has been closed this week in case you're wondering where the next lecture notes are. I'll post some later today, but just an early heads-up that we may not get through the whole series of planned lectures.)

Lecture 14: Cloud computing with Google Compute Engine. raw.githack.com/uo-ec607/lectu…

(Learn how easy it is to start working in the cloud and scale up your analysis with MOAR POWA!)

Lecture 15. High performance computing. docs.google.com/presentation/d…

(Guest lecture by Nick Maggio, director of @uoregon's Research Advanced Computing Services. The lecture focuses on UO's "Talapas" supercomputer cluster, but there's lots of useful HPC info for outsiders too.)

Lecture 16. Databases. raw.githack.com/uo-ec607/lectu…

Learn how to query relational databases directly from R like a boss. Featuring real-life examples from @GlobalFishWatch and other #BigQuery projects.