Coursera – Data Analysis

This course is an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses.

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
Plotting data for exploratory purposes (boxplots, scatterplots, 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.)

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