About Data Camp
Description
Some class sessions have an interactive lesson that you will work through after doing any readings (if applicable). These lessons are a central part of the class—they will teach you how to use R and corresponding packages we’ll use.
Interactive training sections are provided on Data Camp1.
Purpose
The ultimate point of Data Camp is to get you familiarized with an environment that you likely have never seen or been exposed to. While you should absolutely go through each module, there is certainly no expectation that you will get everything right. In fact, the points that you incur don’t mean anything as far as how you are assessed so please use hints as needed! As with any things data science, you’ll learn by doing. If you are a polar personality type when it comes to work (i.e. primarily a perfectionist or mostly careless), then the modules will likely prove to be a challenge. It is highly unlikely that you will be able to comprehend everything by going beyond your limit or that it will just come to you so please work hard but also take breaks, swear2, and ask peers or me for help.
Grading
The emphasis on Data Camp involves putting in a solid effort, rather than completing everything correctly. The earned grade distribution is as follows:
- 115%: Modules are 100% completed. Every task was attempted and answered, and most answers are correct. These are not earned often.
- 100%: Modules are 70–99% complete and most answers are correct or on point. This is the expected level of performance.
- 50%: Modules are less than 70% complete and/or most answers are incorrect or off-point. This indicates that you need to improve next time. Hopefully people will not earn this often.
Otherwise 0%.
Notes
for previous participants
If you have participated in Data Camp before and would like to receive credit for assigned modules you have previously completed, make sure to login with the associated username. Please note that you must do this to qualify.
for new participants
Please take your time going through these, especially the initial module. I suggest setting 8-10 hours aside spread the information out over the next two weeks if it is conducive to your learning style. This first module gives you an understand of the R environment. Please reach out if you need help!
Installing R and RStudio
Go to the Installing R, RStudio, and tidyverse page under Resources to get both R and RStudio installed on your system.
Data Camp Schedule
A growing schedule is given below. The Course and Chapter names represent Data Camp titles3:
| Track | Lesson Page | Assigned | Due by | Required | Course | Chapters covered |
|---|---|---|---|---|---|---|
| 1 | Week 1 | 8/19/21 | 8/25/21 | Introduction to R | Intro to basics | |
| Vectors | ||||||
| Matrices | ||||||
| Factors | ||||||
| Data Frames | ||||||
| Lists | ||||||
| 2 | Week 3 | 9/2/21 | 9/1/21 | Introduction to the Tidyverse | Data wrangling | |
| Data visualization | ||||||
| Grouping and summarizing | ||||||
| Types of visualizations | ||||||
| 3 | Week 3 | 9/3/21 | 9/22/21 | Intermediate R | Conditionals and Control Flow | |
| Loops | ||||||
| Functions | ||||||
| The apply function | ||||||
| Utilities | ||||||
| 4 | Week 3 | 9/3/21 | 10/6/21 | Network Analysis in R | Introduction to networks | |
| Identifying important vertices in a network | ||||||
| Characterizing network structures | ||||||
| Identifying special relationships | ||||||
| EC1 | 8/19/21 | 12/8/21 | Working with Data in the Tidyverse | Explore your data | ||
| EC1 | Tame your data | |||||
| EC1 | Tidy your data | |||||
| EC1 | Transform your data | |||||
| EC1 | ||||||
| EC1 | Dr. Semmelweis and the Discovery of Handwashing | |||||
| EC2 | 8/19/21 | 12/8/21 | Fundamentals of Bayesian Data Analysis in R | What is Bayesian Data Analysis? | ||
| EC2 | How does Bayesian inference work? | |||||
| EC2 | Why use Bayesian Data Analysis? | |||||
| EC2 | Bayesian inference with Bayes’ theorem |