Milestones
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Milestone 5, Final Wrap-Up Deadline: Dec 6 (final deadline) Learning objectives: • Consolidate work and ensure reproducibility. • Reflect on the process, outcomes, and lessons learned. Deliverables: • Finalized documentation and repository. • Any polishing: improved README, visuals, packaging, demo materials. • Git tag for this milestone. • Individual retrospective.
Overdue by 8 month(s)•Due by December 6, 2025•2/2 issues closedMilestone 4, Communicating Results Deadline: Nov 24 Learning objectives: • Communicate research results to a non-technical audience. • Convey the limits of your results and any sources of uncertainty. • Translate new understanding into actionable knowledge. • Tailor communication to a well-defined user/audience, accounting for their capabilities and constraints. • Communicate results and implications quickly and effectively. Deliverables: • Document describing target audience, capabilities, constraints, intended learning, and expected actions (personas helpful). • A communication artifact (website, slide deck, leaflet, WhatsApp campaign, or similar) with justification. • Git tag for this milestone. • Individual retrospective.
Overdue by 8 month(s)•Due by November 24, 2025•3/3 issues closedMilestone 3, Data Analysis Deadline: Nov 10 Learning objectives: • Understand the limitations of data analysis, and how to ask questions that analysis can answer. • Use analysis techniques appropriate to your question, domain, data, and constraints. • Identify, address, and communicate sources of uncertainty in your analysis. • Be prepared to accept undesirable or null results. Deliverables: • Non-technical explanation of findings, including certainty levels and sources of error (with visuals). • Technical description of analysis methods, results, flaws, and alternative approaches. • Scripts and documentation to replicate the analysis. • Git tag for this milestone. • Individual retrospective.
Overdue by 9 month(s)•Due by November 10, 2025•5/5 issues closedMilestone 2, Data Collection Deadline: Oct 20 Learning objectives: • Understand the strengths and weaknesses of modeling the world using data. • Learn to study a domain to identify which data is relevant for your question. • Investigate available data, what is missing, what you could collect yourself, and possible flaws. • Collect, clean, organize, and document a dataset so it is easy to study. Deliverables: • README non-technical explanation of how you model the domain, and possible flaws (visuals helpful). • Dataset documentation (source, structure, flaws, recreation instructions). • All data collection and cleaning scripts, including train/validation split scripts if applicable. • Public hosting of prepared dataset (in repo or external link). • Git tag for this milestone. • Individual retrospective.
Overdue by 9 month(s)•Due by October 20, 2025•6/6 issues closedMilestone 1, Scoping & Planning Deadline: Oct 6 Learning objectives: • Understand how to balance divergent and convergent thinking. • Appreciate the importance of domain expertise in data science. • Identify important problems in a domain that are accessible within your constraints. • Learn how to state a clear research question that can be answered using data science. Deliverables: • Project README with domain description, problem scope, and planned approach. • Repository structure with initial documentation. • Git tag for this milestone. • Individual retrospective.
Overdue by 10 month(s)•Due by October 6, 2025•5/5 issues closed