Learning Objectives

• Compile complete project documentation
• Deliver final presentation
• Reflect on methodology and learnings

1. README.md: Complete project overview
2. docs/business_problem.md: Problem definition and business context
3. docs/data_dictionary.md: Data sources, fields, and transformations
4. docs/methodology.md: Analytical approach and models
5. docs/results.md: Findings, insights, and recommendations
6. docs/deployment.md: How to deploy and maintain
7. Title Slide: Project name, team, date
8. Business Problem: The challenge, why it matters
9. Data Overview: Sources, size, key features
10. Analytics Approach: Methods used, why chosen
11. Key Insights: Top findings with visualizations
12. Business Impact: Quantified results, ROI
13. Recommendations: Actionable next steps
14. Demo: Live walkthrough (3-5 min)
15. Lessons Learned: Challenges, what worked, future directions
16. Q&A: Open for questions

• Business problem clearly defined
• Data collected and documented
• EDA with key visualizations
• Predictive/prescriptive model built
• Business insights and recommendations
• Dashboard or API deployed
• Complete README with setup
• Data dictionary documented
• Methodology explained
• Results quantified with business metrics
• Presentation slides ready
• Demo video (5-7 min)
• Code on GitHub with license
• Requirements.txt/environment.yml
• Project retrospective
• Self-assessment rubric completed

Q1: How to quantify business impact of data project?

Revenue increase (by X%), Cost reduction (by $Y), Time saved (Z hours/week), Risk reduction (by W%). Use A/B test or before/after comparison. Q2: What to include in project retrospective?

What went well, what could be improved, what surprised us, what would we do differently. Capture lessons for next project. Q3: How to demonstrate data literacy in presentation?

Choose right visuals, explain methodology at appropriate level, interpret results correctly, acknowledge limitations, connect findings to business strategy. Q4: What is a data product?

Analytics product delivering data-driven insights to users. Can be dashboard, API, report, or embedded analytics. Should be reliable, accessible, and actionable. Q5: How to ensure project sustainability?

Document everything, automate pipeline, set up monitoring, plan for model updates, transfer knowledge to business team, build for maintainability not just features. Q6: Self-assessment rubric:

markdown
| Criteria | Excellent (4) | Good (3) | Fair (2) | Poor (1) |
|----------|---------------|----------|----------|----------|
| Problem Definition | Clear, specific, measurable | Clear but broad | Vague | Missing |
| Data Analysis | Comprehensive EDA, visualizations | Good analysis | Basic | None |
| Model/Methodology | Appropriate, well-tuned | Reasonable choice | Misaligned | Missing |
| Results | Quantified business impact | Good insights | Superficial | None |
| Presentation | Professional, compelling | Clear organization | Adequate | Poor |

Q7: How to handle questions you don't know?

Be honest ("I don't know but I can find out"), redirect to what you do know, show thinking process ("Here's how I would approach finding the answer"), ask clarifying questions. Q8: Final reflection questions:

• What was the hardest part of the project and why?
• What would you do differently with more time?
• What was the most valuable thing you learned?
• How will this project be useful in your career? Join Discord PreviousMilestone 7: Deployment & Operations