Build an End-to-End Data Science Project
14 weeks · 5 milestones
Complete a full data science project — from a real stakeholder question to a deployed model to a decision a non-technical person acted on. Every step documented and verifiable. No Iris, no Titanic.
Milestone map
Milestone map
3 milestones
A data science project that starts with a dataset instead of a question produces answers nobody asked for. Start with a specific, answerable question — one that matters to a real stakeholder or domain — then find or collect data that could answer it. Complete a thorough exploratory data analysis: distributions, missingness, correlations, and at least one surprising or counter-intuitive finding. The EDA should inform your modelling choices, not just precede them.
Proof required
Share a public Jupyter notebook or GitHub repository containing: (1) a one-paragraph problem statement naming the question, the intended audience or stakeholder, and why the answer matters, (2) a description of the data source with the retrieval or collection method documented, and (3) an EDA section with at least 5 visualisations and a written finding for each — including one finding that surprised you.
What gets checked
- Problem statement names a specific question with a measurable answer — 'I explored sales data' is not a question; 'Which product categories have the highest return rate, and is return rate correlated with discount depth?' is
- EDA visualisations are annotated with findings, not just displayed — a chart with no interpretation shows you can run code, not that you understand the data
- At least one surprising finding is documented with an explanation of why it was unexpected and what it implies for the next step