Chapter One - A Journey with DataKind SG
Welcome
Goals
1
Overview of Project Flow
1.1
Project Funnel
1.2
Project Accelerators
1.3
DataJams
1.4
DataDive
1.5
DataCorps
1.6
DataLearn
2
Project Management
2.1
Tracking Progress
2.2
Tasks and Assignment
2.3
Recommended Roles
2.4
Tips
3
Data Cleaning
3.1
Test Driven Data Cleaning
3.1.1
For Volunteers
3.1.2
For Team Leads
4
Managing Code
4.1
Version Control
4.2
Github FAQs
4.2.1
The csv.gz files seems to be too small/corrupted, what should I do?
4.2.2
I’m new to Git, where can I get more information?
4.2.3
How to clone a git repository?
4.2.4
How to load csv.gz files?
4.2.5
How can I safely edit the code and data files?
4.2.6
Doing a git branch
4.2.7
How to submit changes to the code repository
4.3
Directory Structure
4.4
Code Style
4.5
Preamble information to be placed at the top of every script
4.6
Quickstart Templates
4.7
Tips
5
Docker Pipeline for Reproducible Research
5.1
Overview
5.2
Prerequisites
5.3
Task 1: Setup Dockerfile
5.4
Task 2: Setup CI and Build Docker Image
5.5
Task 3: Curate Deliverables
5.6
Task 4: Resolve Environment Issue
6
Data Protection and Information Ethics
6.1
Data Protection
6.2
Information Ethics
7
Resources
7.1
Tools
7.1.1
Test Driven Data Cleaning
7.1.2
Github
7.1.3
Trello
7.2
Cheatsheets
7.2.1
R
7.2.2
Python
7.3
Videos
8
About
8.1
About DKSG
8.2
Contributors
References
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Chapter One - A Journey with DataKind SG
References