Become Data Analyst
12 weeks · 4 milestones
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3 milestones
Choose a real dataset — from your work, from a public source (Kaggle, data.gov, Google Dataset Search), or from a domain you know well — that contains at least 10,000 rows and a meaningful question worth answering. Perform a complete end-to-end analysis: load and clean the data, explore distributions, identify and handle missing values and outliers, answer one specific question using at least 2 different visualisations, and write a 300-word summary of what you found and what you cannot conclude from this data alone.
Proof required
Submit a public Kaggle notebook, a GitHub repository with a Jupyter notebook, or a Google Colab link showing: the data loading and cleaning steps (with code), at least 2 visualisations addressing the question, and the 300-word written summary. The dataset must be identified by name and source.
What gets checked
- Code is reproducible — someone else running it top-to-bottom gets the same results; random seeds set where relevant
- Cleaning decisions are documented with code comments — not just the code, but why each decision was made (e.g. 'rows with > 30% nulls dropped, 847 rows removed')
- Written summary explicitly names what the data cannot conclude — not just what was found; the limits section is the indicator of analytical maturity