Data Science using R (2018)

Course plan

Below is the intended course plan (some deviations can be expected).

First week

Mon 20 Aug Tue 21 Aug Wed 22 Aug Thur 23 Aug Fri 24 Aug
MORNING
 R101 (R intro)
RStudio 		Rmarkdown:
formats and add-ons
 ggplot2 I
 Clustering:
kmeans, hclust 		Classification:
Logistic regression
Summarising data
Base graphics 		PC1 Analysis
PC1 Regression 		Data wrangling
dplyr, tidyr 		Programming in R Naive Bayes
Support Vector Machines
AFTERNOON
 Linear models and lm
factor variables 		Regularised regression
glmnet and rpart 		ggplot2 II
 Reproducible
research with R 		Misc topics:
Rcpp, dbplyr, ...
Predictions from lm Variance-bias trade-off
Cross-validation 		Animations with R Shiny:
apps and dashboards 		Misc topics:
Rcpp, dbplyr, ...

1: Principal Components

Last day

The last day (Tuesday Sep 4) will be dedicated to more perspectives on linear models, including confidence intervals confint, bootstrapping and caveats.

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