This content is from the fall 2016 version of this course. Please go here for the most recent version.

Date	Topic	Lab
Mon Sep 26	cm001: Introduction to computing for the social sciences
Wed Sep 28	cm002: Visualizations and the grammar of graphics	lab01: Software setup
Mon Oct 3	cm003: Data transformation and exploratory data analysis
Wed Oct 5	cm004: Data wrangling	lab02: R Markdown, git, and stuff
Mon Oct 10	cm005: Pipes and functions in R
Wed Oct 12	cm006: Vectors and iteration	lab03: Using Git in Bash
Mon Oct 17	cm007: Model building
Wed Oct 19	cm008: Reproducibility in research	lab04:
Mon Oct 24	cm009: Introduction to Python
Wed Oct 26	cm010: More introduction to Python	lab05:
Mon Oct 31	cm011: Getting data from the web: API access
Wed Nov 2	cm012: Getting data from the web: scraping	lab06: Webdata in Python
Mon Nov 7	cm013: Text analysis: fundamentals and sentiment analysis
Wed Nov 9	cm014: Text analysis: topic modeling	lab07: Text analysis in Python
Mon Nov 14	cm015: Statistical learning: basics and classification problems
Wed Nov 16	cm016: Statistical learning: resampling and tree-based methods	lab08: R Markdown websites (and more!)
Mon Nov 21	cm017: Distributed learning: relational data
Wed Nov 23	cm018: Distributed learning: parallel computing	lab09:
Mon Nov 28	cm019: Building Shiny apps
Wed Nov 30	cm020: Building Shiny apps (part II)	lab10:

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