Johan A. Dornschneider-Elkink -

Data Analytics for the Social Sciences

POL 30660 Data Analytics for the Social Sciences

Friday, 9-11 am, B110 Newman Building (note final session in B109).

This module is co-taught by Yoo Sun Jung (first half) and Jos Elkink (second half).

You should install the necessary software on your computer prior to the first class. All software is free. R can be downloaded here.

You should also install RStudio, after installing R.

A handy overview of R regression commands can be found here: reference card for regression. A more general one for R is here: short reference card. The Google R Style guide provides suggestions for writing clear code.

1	21/1	Introduction	slides	lab	output	lecture: introduction \| live session videos: markdown \| importing Excel \| using packages markdown cheat sheet
2	28/1	Distributions and descriptive statistics	slides	lab	output	lecture: levels of measurement \| missing data \| graphs \| descriptive statistics
3	4/2	Comparing through visualisation	slides	lab	output	lecture: multiway graphs \| control variables \| project 1 Top 50 ggplot examples
	11/2	Group presentations
4	18/2	Linear regression	slides	lab	output	lecture: linear regression \| multiple regression \| model selection videos: recoding and merging (live session) \| merging (demonstration)
5	25/2	Logistic regression	slides	lab	output	lecture: linear probability model \| linear discriminant analysis \| logistic regression \| model fit
6	4/3	Trees and forests	slides	lab	output	lecture: trees \| forests \| project 2 (live)
7	25/3	Cluster analysis	slides	lab	output	lecture: intro \| kmeans \| hierarchical \| dissimilarity \| speeches
8	1/4	Dimension reduction	slides	lab	output	lecture: intro \| mds \| pca \| factor analysis
9	8/4	Wordscores	slides	lab	output	lecture: intro \| wordscores \| wordfish
	15/4	Good Friday
10	22/4	Topic models	slides	lab	output	lecture: topic models \| putin \| afterthoughts \| project 3