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Collaborative Data Science Practices
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Table of contents
1
Introduction
2
Coding Principles
3
Architecture Principles
4
Prototypical R File
5
Prototypical SQL File
6
Prototypical Repository
7
Data at Rest
8
Patterns
9
Security & Private Data
10
Automation & Reproducibility
11
Scaling Up
12
Parallel Collaboration
13
Documentation
14
Style Guide
15
Publishing Results
16
Validation
17
Testing
18
Troubleshooting and Debugging
19
Workstation
20
Considerations when Selecting Tools
21
Growing a Team
22
Material for REDCap Users
23
Material for REDCap Developers
24
Material for REDCap Admins
Appendix
A
Git & GitHub
B
Regular Expressions
C
Snippets
D
Presentations
E
Scratch Pad of Loose Ideas
F
Example Dashboard
G
Example Chapter
H
Acknowledgements
I
References
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21
Growing a Team
21.1
Recruiting
21.2
Training to Data Science
Starting with a Researcher
Starting with a Statistician
Starting with a DBA
Starting with a Software Developer
21.3
Bridges Outside the Team
Monthly User Groups
Annual Conferences
20
Considerations when Selecting Tools
22
Material for REDCap Users
On this page
21
Growing a Team
21.1
Recruiting
21.2
Training to Data Science
21.3
Bridges Outside the Team
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