Data Science Boot Camp
Course coverage
Core texts and resources
Topics
Schedule
1
Computing setup
1.1
R and related resources
1.1.1
Install R
1.1.2
Install R Studio
1.1.3
Install the Tidyverse packages for R
1.1.4
Install LaTeX
1.2
Git and Github
1.2.1
Install git and link tit to R Studio
1.3
Other resources
2
Introduction
2.1
Reproducible workflows
2.1.1
The concept of reproducibility
2.1.2
How we create reproducibility in practice
2.2
Working collaboratively in a team
3
Materials from
Basecamp
4
Data Science with R
5
Data pipelines
5.1
Retrieving data
5.1.1
From local files
5.1.2
From APIs
5.1.3
From databases
5.1.4
From web resources
5.2
Data types
5.2.1
Data types in R
5.2.2
Conversion on read-in
5.3
Data wrangling
5.3.1
Tidy data
5.3.2
Dplyr
5.4
Managing data
5.4.1
DOs and DON’Ts
6
Git and Github
6.1
Version control: what and why
6.2
How git works
6.2.1
The .gitignore file
6.3
Git in R Studio
6.4
Github
6.4.1
Using personal tokens to access Github
6.5
Best practices
7
R Markdown
8
Shared resources
8.1
Network File Server
8.1.1
Instructions for mounting network file server on your own machine
9
Bibliographic resources
9.1
Zotero
9.2
Bibtex
References
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Data Science Boot Camp
Chapter 4
Data Science with R
Main reference:
R for Data Science
by Hadley Wickham and Garrett Grolemund.