What is coursera-datasharing?

tanykim/coursera-datasharing — explained in plain English

Analysis updated 2026-07-17 · repo last pushed 2013-11-25

Audience · researcherComplexity · 1/5DormantSetup · easy

Mindmap

mindmap
  root((repo))
    What it does
      Guide for sharing data
      Explains tidy data format
      Prevents wasted time
    Deliverables
      Raw data
      Tidy data set
      Code book
      Processing recipe
    Use cases
      Prep data for a statistician
      Avoid messy Excel handoffs
      Ensure reproducible results
    Audience
      Researchers
      Students
      Scientists
    Context
      Coursera course material
      Common data mistakes
      Data documentation tips

mindmap root((repo)) What it does Guide for sharing data Explains tidy data format Prevents wasted time Deliverables Raw data Tidy data set Code book Processing recipe Use cases Prep data for a statistician Avoid messy Excel handoffs Ensure reproducible results Audience Researchers Students Scientists Context Coursera course material Common data mistakes Data documentation tips

What do people build with it?

USE CASE 1

Prepare a tidy dataset and code book before sending data to a statistician for analysis.

USE CASE 2

Document the exact steps used to transform raw data into a clean, analyzable format.

USE CASE 3

Avoid common data-sharing mistakes like messy multi-sheet Excel files with colored cells.

How does it compare?

	tanykim/coursera-datasharing	0verflowme/alarm-clock	0xhassaan/nn-from-scratch
Stars	—	—	0
Language	—	CSS	Python
Last pushed	2013-11-25	2022-10-03	—
Maintenance	Dormant	Dormant	—
Setup difficulty	easy	easy	moderate
Complexity	1/5	2/5	4/5
Audience	researcher	vibe coder	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

Copy-paste prompts

Prompt 1

Help me turn my raw survey data into a tidy dataset following the coursera-datasharing guide's format.

Prompt 2

Write a code book for my dataset explaining each variable, its units, and how it was collected.

Prompt 3

Review my data-cleaning script and turn it into a clear recipe another researcher could follow, per this guide's recommendations.

Frequently asked questions