Data Analysis Project (Peer-Reviewed, Dataset + Findings)
10 weeks · 3 milestones
Go from spreadsheets to Python pandas and produce real insights.
Milestone map
Milestone map
3 milestones
Data analysis begins with wrangling — getting a raw dataset into a form where questions can be asked of it. Choose a publicly available raw dataset (from Kaggle, data.gov, Our World in Data, or a government open data portal) and perform documented cleaning in a Jupyter or Colab notebook: handle missing values, fix data type errors, identify and document outliers, and produce a cleaned version with a written explanation of every transformation and the reason for each decision. The notebook must be publicly shareable and run top-to-bottom without errors.
Proof required
Submit a public Jupyter notebook or Google Colab link (shareable URL, not a screenshot) showing the raw dataset, every cleaning step with Python code and a written text-cell explanation for each decision, and summary statistics (shape, dtypes, null counts) at the start and end of the notebook demonstrating the before/after state of the data.
What gets checked
- Notebook is publicly accessible via a shareable link and runs from top to bottom without errors — import errors, missing file references, or runtime errors on standard libraries indicate incomplete preparation
- Every cleaning decision has a text-cell written explanation, not just code — removing a row, imputing a value, and changing a dtype each have a written justification; code-only notebooks without prose explanation do not pass
- Summary statistics at the start and end of the notebook demonstrate the before/after state numerically — shape, null counts, and dtype table showing what changed