Course Content:
The structure of a data analysis (steps in the process, knowing when to quit, etc.)
Types of data (census, designed studies, randomized trials)
Types of data analysis questions (exploratory, inferential, predictive, etc.)
How to write up a data analysis (compositional style, reproducibility, etc.)
Obtaining data from the web (through downloads mostly)
Loading data into R from different file types
Descriptionting data for exploratory purposes (boxDescriptions, scatterDescriptions, etc.)
Exploratory statistical models (clustering)
Statistical models for inference (linear models, basic confidence intervals/hypothesis testing)
Basic model checking (primarily visually)
The prediction process
Study design for prediction
Cross-validation
A couple of simple prediction models
Basics of simulation for evaluating models
Ways you can fool yourself and how to avoid them (confounding, multiple testing, etc.)