Understand critical programming language concepts

Configure statistical programming software

Make use of R loop functions and debugging tools

Collect detailed information using R profiler

Understand common data storage systems

Apply data cleaning basics to make data "tidy"

Use R for text and date manipulation

Obtain usable data from the web, APIs, and databases

Understand analytic graphics and the base plotting system in R

Use advanced graphing systems such as the Lattice system

Make graphical displays of very high dimensional data

Apply cluster analysis techniques to locate patterns in data

Organize data analysis to help make it more reproducible

Write up a reproducible data analysis using knitr

Determine the reproducibility of analysis project

Publish reproducible web documents using Markdown

Understand the process of drawing conclusions about populations or scientific truths from data

Describe variability, distributions, limits, and confidence intervals

Use p-values, confidence intervals, and permutation tests

Make informed data analysis decisions

Use regression analysis, least squares and inference

Understand ANOVA and ANCOVA model cases

Investigate analysis of residuals and variability

Describe novel uses of regression models such as scatterplot smoothing

Use the basic components of building and applying prediction functions

Understand concepts such as training and tests sets, overfitting, and error rates

Describe machine learning methods such as regression or classification trees

Explain the complete process of building prediction functions

Develop basic applications and interactive graphics using GoogleVis

Use Leaflet to create interactive annotated maps

Build an R Markdown presentation that includes a data visualization

Create a data product that tells a story to a mass audience

Create a useful data product for the public

Apply your exploratory data analysis skills

Build an efficient and accurate prediction model

Produce a presentation deck to showcase your findings