Business Problem — COVID-19 Data Analysis¶
Context¶
You're a data analyst at a public health think tank producing weekly briefings for policymakers and journalists. The briefings must be accurate, comparable across countries, and free of the distortions that plagued much early pandemic reporting (raw case counts that ignored population, testing differences, and reporting lags).
The challenge: raw COVID numbers are misleading. The US having more total cases than Iceland says nothing about which country handled the pandemic better — the US has 1,000× the population. Comparable, honest analysis requires per-capita normalisation, rolling averages, and careful caveating.
Analytical Questions to Answer¶
Q1 — Global Progression¶
- How did global cases and deaths evolve over time?
- Can we identify the distinct "waves" of the pandemic?
- When did each wave peak globally?
Q2 — Fair Country Comparison¶
- Which countries had the highest cases per capita (not in absolute terms)?
- Which had the highest deaths per capita?
- How does the case fatality rate (deaths / cases) vary across countries?
Q3 — Wave Analysis¶
- For a selected set of countries, when did each wave hit?
- How did the severity of waves change over time?
- Did later waves have lower fatality rates (better treatment, vaccines)?
Q4 — Vaccination Impact¶
- How did vaccination rollout progress across countries?
- Is there a visible relationship between vaccination rate and death rate in later waves?
Q5 — Communication¶
- How do we present this data without sensationalising or downplaying?
- What caveats must accompany every comparison?
Why This Project Matters for Analysts¶
COVID-19 data is a masterclass in the things that separate good analysts from dangerous ones:
| Trap | Lesson |
|---|---|
| Raw counts ignore population | Always normalise per capita for comparison |
| Cumulative ≠ daily | Know whether your metric is a running total or a daily value |
| Reporting noise (weekend dips) | Use 7-day rolling averages |
| Testing differences | Cases depend on testing — caveat heavily |
| Cherry-picked date ranges | Show full context, not convenient windows |
| Correlation ≠ causation | Vaccination correlations need careful framing |
Stakeholders¶
| Stakeholder | Needs |
|---|---|
| Policymakers | Accurate, comparable trends to inform decisions |
| Journalists | Clear visuals with honest caveats |
| The Public | Trustworthy information, not fear or false reassurance |