GitHub - earino/designing-analytics-projects: Course materials for Designing Analytics Projects (ECBS5228A) - Central European University

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Designing Analytics Projects

A practical framework for analytics projects that deliver business value.

Course materials for ECBS5228A at Central European University's MS in Business Analytics program.

What's This Course About?

Most analytics courses teach you how to analyze data. This course teaches you how to design analytics projects that matter — the work that happens before you write any code.

You'll learn to ask:

  • What decision will this analysis inform?
  • What metric are we optimizing, and what breaks if we succeed?
  • Who needs to buy in, and who might block us?

The course is built around a single artifact: the Analytics Project Brief. This one-page framework forces clarity on problem definition, metrics, stakeholders, methodology, and success criteria before any analysis begins.

What's Included

├── slides/                    # 6 teaching blocks (100 min each)
│   ├── block_01_*            # The Analytics Project Brief framework
│   ├── block_02_*            # Acquisition Analyses
│   ├── block_03_*            # Retention & Growth Analyses
│   ├── block_04_*            # Application & Practice
│   ├── block_05_*            # Stakeholders & Influence
│   └── block_06_*            # Capstone Preparation
│
├── templates/                 # The Brief framework
│   ├── analytics_project_brief.md    # Blank template
│   └── examples/              # 9 worked examples
│
├── scenarios/                 # 18 company case studies for practice
├── figures/                   # Slide images and visuals
├── weekly_writeups/           # Student assignment prompts
├── syllabus.md               # Full course syllabus
└── scripts/                   # Utilities (slide overflow checker)

The Analytics Project Brief

The centerpiece of this course is a 10-section framework:

Section Key Question
1. Problem & Decision What decision will this analysis inform?
2. Metrics What's the primary metric? What are the counter-metrics?
3. Stakeholders Who has power/interest? Who might block?
4. Methodology What analyses will we run?
5. Scope & Deliverables What's in and out of scope?
6. Success Criteria What does success look like?
7. Timeline What are the key milestones?
8. Risks & Assumptions What could go wrong?
9. Ethics & Privacy Any PII or bias concerns?
10. Pre-Mortem It's 3 months from now and this failed. What happened?

The 9 Foundational Analyses

The course covers analyses across the customer journey:

Acquisition

  • Funnel Analysis
  • Channel Attribution
  • Campaign Effectiveness
  • CAC/LTV Analysis

Retention

  • Retention Analysis
  • Power User Analysis
  • Failure Analysis

Growth

  • Expansion & Monetization
  • Ecosystem Analysis

Each analysis has a worked Brief example in templates/examples/.

Using These Materials

For Instructors

The slides are built with Marp (Markdown Presentation Ecosystem).

To view with presenter notes:

  1. Open any .html file in a browser
  2. Press p to enter presenter mode
  3. Comprehensive instructor notes appear below each slide

To modify and rebuild slides:

# Install Marp CLI
npm install -g @marp-team/marp-cli

# Rebuild a single deck
marp slides/block_01_analytics_project_brief.md --html -o slides/block_01_analytics_project_brief.html

# Check for content overflow
pip install -r requirements.txt
python scripts/check_overflow.py

Course structure:

  • 2 days × 3 blocks × 100 minutes = 600 minutes total
  • Day 1: The Framework & Analyses (Blocks 1-3)
  • Day 2: Application & Influence (Blocks 4-6)

For Self-Study

  1. Start with the syllabus (syllabus.md) to understand the course structure
  2. Read the Brief template (templates/analytics_project_brief.md)
  3. Work through the examples in templates/examples/ — one for each analysis type
  4. Practice with scenarios in scenarios/ — complete Briefs for these cases
  5. Review the slides for deeper explanation of each concept

Key concepts to master:

  • Counter-metrics and the "What Breaks" framework
  • Stakeholder mapping (Power-Interest Grid)
  • Pre-mortem thinking
  • The difference between correlation and causation in analytics recommendations

Suggested Reading

Reading Time Purpose
Designing Experimentation Guardrails — Airbnb Engineering ~15 min Counter-metrics framework
Data Analyst Guide to Stakeholder Management — Towards Data Science ~12 min Stakeholder mapping

Recommended Books:

  • Getting to Yes (Chapters 1-3) — Fisher, Ury, Patton — Read before the influence and negotiation content
  • Click Here: The Art and Science of Digital Marketing and Advertising — Alex Schultz — Meta's CMO on digital marketing fundamentals, incrementality measurement, and attribution

License

This work is licensed under CC-BY-4.0 (Creative Commons Attribution 4.0 International).

You are free to:

  • Share — copy and redistribute the material
  • Adapt — remix, transform, and build upon the material for any purpose

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Author

Eduardo Arino de la Rubia (rubiae@ceu.edu)

Data science leader based in southern Spain. Former Senior Director of Data Science at Meta, Chief Data Scientist at Domino Data Lab (pre-seed through Series B), and Principal Data Scientist at Ingram.

Contributing

Found an error? Have a suggestion? Issues and pull requests are welcome.

For questions about using these materials in your own teaching, feel free to reach out.