Data Science Boot Camp
1
Overview
1.1
Texts and resources
1.1.1
Core texts and resources
1.1.2
Supplemental texts and resources
1.2
Schedule of topics
1.3
Credits
2
Computing setup
2.1
R and related resources
2.1.1
Install R
2.1.2
Install R Studio
2.1.3
Install the Tidyverse packages for R
2.1.4
Install LaTeX
2.2
Git and Github
2.2.1
Install git and link tit to R Studio
2.3
Other resources
3
Data Science on the CCPS Team
3.1
What is data science?
3.2
Data science projects and products
3.2.1
Decision tools and dashboards
3.2.2
Publications
3.2.3
Research
3.3
Shared resources
3.4
Working collaboratively in a team
3.5
Reproducible workflows
3.5.1
The concept of reproducibility
3.5.2
How we create reproducibility in practice
4
Data pipelines
4.1
Retrieving data
4.1.1
From local files
4.1.2
From APIs
4.1.3
From databases
4.1.4
From web resources
4.2
Data types
4.2.1
Data types in R
4.2.2
Conversion on read-in
4.3
Data wrangling
4.3.1
Tidy data
4.3.2
Dplyr
4.4
Managing data
4.4.1
DOs and DON’Ts
5
Data wrangling
5.1
Tidy data
5.2
Dplyr
5.3
Managing data
5.3.1
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
Published with bookdown
Data Science Boot Camp
Chapter 9
Bibliographic resources
9.1
Zotero
9.2
Bibtex