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CCDS

For the collective campus data science course.

Week One: Data Munging

Introduction to R Reading in data from csv files Introduction to databases Extracting data from databases Merging data tables Tidy data Writing functions The split-apply-combine strategy using dplyr Generating summary statistics for arbitrary sub-groups Writing data to files & databases

Week Two: Prediction

Introduction to predictive modelling Structural modelling vs machine learning Predicting different data types Building a predictive model using linear regression Under the hood: maximum likelihood Feature selection & prediction using regularised GLMs A brief introduction to Bayesian modelling Classification and regression trees Boosted trees and Random Forests

Week Three: Causality

What is causality, and why won’t predictive models help me? Data generating processes and observational equivalence Unobserved data and simultaneity The experimental ideal Natural experiments as a way of thinking about the world Instrumental variables Other techniques (Difference-in-differences, regression discontinuity) Matching routines

Week Four: Visualisation

Introduction to ggplot2 Aesthetics – x, y, size, weight, group, colour, fill, etc. Chart types Data exploration using faceting and grouping Customising chart appearance Publishing work using Rmd and Rpres Celebratory breakup drinks (!)

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