Before you start browsing through the material consider the following strategies: There are (at least) two ways to progress through this material, depending upon how much time you have to devote to it each week.

  1. If you are self-paced with at least 10 hours a week to devote to learning R, I’d recommend adopting the schedule below, which is designed for an intense but doable semester-long course, one module per week. It is intended to take the average graduate student roughly 10 hours per week to complete all required tasks. However, some students will find programming to be more challenging and may take up to 15 hours per week. Some will fly through the material in 5.

  2. If you are self-paced with limited free time, you can take a more relaxed pace by alternating weeks: in the first week in each pair, complete the DataCamp materials, and in the second week, complete the project.

I recommend that you work your way through the entire series. After working through each module, I strongly advise that you put your new skills to the test with the DataCamp courses and projects. These courses will significantly increase your learning curve since learning R is very much like learning a new language and you will need a lot of practising to master it!

Module Topic DSbook Chapter datsci Module Assessment DataCamp Practice DataCamp Project
0. Introduction & Software Getting started with R datsci_00 Available by trimester start DataCamp 1: Introduction to R
1. R Basics R basics datsci_01 Available by trimester start DataCamp 2: Intermediate R
2. Data Visualizations Visualization datsci_02 Available by trimester start DataCamp 3a: Intro DataViz ggplot2
DataCamp 3b: Intermed. DataViz ggplot2		 PROJECT 1: Visualizing COVID-19
PROJECT 2: Phyllotaxis: Draw Flowers Using Mathematics
3. Probability Probability datsci_03 Available by trimester start DataCamp 4a: Intro to statistics with R
DataCamp 4b: Foundations of probability		 PROJECT 1: Kidney Stones and Simpson’s Paradox
PROJECT 2: Health Survey Data Analysis of BMI
4. Inference & Modeling Statistical Inference datsci_04 Available by trimester start DataCamp 5a: Foundations of inference
DataCamp 5b: Inference linear regression		 PROJECT 1: Leveling in Candy Crush Saga
PROJECT 2: Modeling the Volatility of US Bond Yields
5. Productivity Tools Productivity Tools datsci_05 Available by trimester start DataCamp 6a: Reporting with R Markdown
DataCamp 6b: Introduction to Shell/UNIX
6. Wrangling Data Wrangling datsci_06 Available by trimester start DataCamp 7a: Introduction to tidyverse
DataCamp 7b: Data manipulation with R
DataCamp 7c: Text mining with R
DataCamp 7d: Working with dates & times		 PROJECT 1: Visualizing Musical Data
PROJECT 2: A Text Analysis of Trump’s Tweets
PROJECT 3: Going Down to South Park: A Text Analysis
7. Linear Regression Regression datsci_07 Available by trimester start DataCamp 8a: Correlation and Regression
DataCamp 8b: Multiple & logistic regression		 PROJECT: Trends in Maryland Crime Rates
8. Machine Learning Machine Learning datsci_08 Available by trimester start DataCamp 9: Machine learning fundamentals PROJECT 1: What Makes a Pokémon Legendary?
PROJECT 2: Predict Taxi Fares with Random Forests
PROJECT 3: The Impact of Climate Change on Birds

Resources:

  1. Irizarry, R. (2019). Introduction to Data Science. Data Analysis and Prediction Algorithms with R. eBook available at https://rafalab.github.io/dsbook/