Public Health Data Communication
6 weeks · 0 milestones
Create a data visualisation or infographic communicating a real public health finding to a non-technical audience. Proof requires: (a) the published visualisation (public URL or uploaded file); (b) the underlying dataset with documented source and data cleaning steps; and (c) a written explanation of the design choices made specifically for a non-technical audience (why this chart type, why these colours, what was simplified and what was omitted). Reviewed by a public health professional. The dataset must be real — not synthetic or hypothetical.
Milestone map
Milestone map
3 milestones
Obtain a real public health dataset from a freely available source (PHE Fingertips, ONS, WHO Global Health Observatory, CDC WONDER, NCHS, or equivalent). Choose a dataset with at least 500 rows and at least 5 variables. Conduct data cleaning: identify and handle missing values, remove duplicates, correct data type inconsistencies, and document your decisions. Produce a data cleaning log (1 page) and a clean dataset summary (row count, variable types, missingness summary after cleaning).
Proof required
Submit your data cleaning log and dataset summary. The log must document each decision made (what was found, what was done, why). Submit the first 20 rows of the cleaned dataset as a table.
What gets checked
- Data source is a real published public health dataset — not a simulated or fabricated dataset.
- Cleaning log documents the decision for each issue — 'duplicates removed' is not a decision; 'found 3 duplicate patient IDs where data for the same patient appeared twice with different dates; kept the most recent record' is.
- Missing data handling is documented — different strategies for missing data (removal, imputation, flagging) must be justified based on the variable's role.