Data Literacy Foundations
Learn to read charts critically, clean a real dataset, and write up what it shows, with free tools and open data.
Dataset Cleaning Project
A project in which the learner cleans a real dataset, documents every change, and writes a short findings note.
- Kind
- project
- Status
- draft · v0.1.0
- License
- CC BY-SA 4.0
Outcomes measured
Evidence required
- The raw dataset (a few hundred to a few thousand rows) and its source.
- The cleaned dataset.
- A change log in which every removed or altered row is accounted for, sufficient for a reviewer to reproduce the cleaning.
- A one-page findings note with at least one chart, the summary statistics it rests on, and a plain-language conclusion.
Task
Choose an open dataset with real problems in it: missing values, inconsistent dates or text, duplicates. Clean it using a spreadsheet or a free scripting tool, logging every change. Then answer one question about the data with summary statistics and a single chart, and write it up for a non-expert.
Scoring guide
| Level | Description |
|---|---|
| Proficient | The change log fully reproduces the cleaning. Statistics are correct. The chart fits the question and the data. A non-expert can follow the note. |
| Developing | Cleaning is sound but the log has gaps, or the chart or statistics do not quite fit the question. |
| Beginning | Changes are undocumented, statistics are incorrect, or the note does not reach a conclusion. |
Only Proficient work counts as evidence for the Data Literacy Foundations credential.
Where this assessment is used
Courses that prepare for it
Credentials that require it
Data Literacy Foundations
The holder can read charts critically, clean a small dataset with a reproducible change log, and summarize and communicate findings to a non-expert.