Project 16: Strong Data Scientist Capstone

Build an integrated decision system combining GLMs, mixed models, advanced forecasting, hierarchical Bayes, and generalization-aware validation.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 4-6 weeks
Main Programming Language Python + SQL
Alternative Programming Languages R/Stan
Coolness Level Level 5: Pure Magic
Business Potential 2. The “Micro-SaaS / Pro Tool”
Prerequisites Projects 6-15
Key Topics GLM, mixed models, advanced TS, Bayesian hierarchical models, learning-theory basics

1. Learning Objectives

  1. Select advanced model families by target and data hierarchy.
  2. Integrate frequentist and Bayesian evidence into one decision packet.
  3. Validate subgroup stability and generalization risk.
  4. Produce policy simulation with uncertainty-aware recommendations.

2. All Theory Needed (Per-Concept Breakdown)

2.1 Advanced Model Families

  • Fundamentals: GLMs and mixed/hierarchical models respect non-Gaussian targets and grouped data.
  • Deep Dive into the concept: Partial pooling prevents overfitting sparse groups and improves decision stability.

2.2 Generalization and Decision Robustness

  • Fundamentals: Model complexity must be balanced against out-of-sample behavior.
  • Deep Dive into the concept: Learning-theory intuition (bias/variance/capacity) and robust validation prevent brittle policies.

3. Project Specification

3.1 What You Will Build

A capstone pipeline that outputs one executive policy recommendation with uncertainty bounds across regions and future scenarios.

3.2 Functional Requirements

  1. GLM module for count/binary outcomes.
  2. Mixed model module for grouped effects.
  3. Hierarchical Bayesian module for partial pooling and posterior forecasts.
  4. Advanced TS forecasting module with structural-change checks.
  5. Cross-model policy simulation and decision packet generation.

3.3 Non-Functional Requirements

  • Reproducible model registry and assumptions ledger.
  • Decision packet suitable for executive review.

3.4 Example Usage / Output

$ python ds_capstone.py --scenario global_pricing
GLM demand elasticity estimated for 12 regions
Mixed model region variance: 0.43
Posterior P(policy improves margin): 0.948
8-week forecast interval coverage: 93.1%
Expected annual margin delta: +$2.8M (P10:+$0.9M, P90:+$4.4M)

3.5 Real World Outcome

You produce a production-style statistical recommendation package with quantified upside, downside, and assumption sensitivities.

4. Solution Architecture

Data contracts -> model family modules (GLM/mixed/Bayes/TS) -> simulation -> governance checks -> final packet

5. Implementation Guide

5.1 Development Environment Setup

pip install numpy pandas statsmodels pymc

5.2 Project Structure

P16/
  ds_capstone.py
  models/
  simulations/
  outputs/
  governance/

5.3 The Core Question You Are Answering

“Can I provide a robust, uncertainty-aware decision that holds across model families and subgroup structure?”

5.4 Concepts You Must Understand First

  1. GLM link functions and distribution choices
  2. Mixed effects and partial pooling
  3. Advanced forecasting diagnostics
  4. Bayesian hierarchical posterior interpretation
  5. Bias-variance/generalization intuition

5.5 Questions to Guide Your Design

  1. Which model family maps to each target and hierarchy?
  2. Which diagnostics are release blockers?
  3. How will you reconcile conflicting model conclusions?

5.6 Thinking Exercise

Draft a governance card listing assumptions, diagnostics, failure triggers, and rollback criteria for each module.

5.7 The Interview Questions They’ll Ask

  1. Why do mixed models outperform fixed-only models for sparse groups?
  2. What is partial pooling in practical terms?
  3. How do you detect structural breaks in forecasting?
  4. How do you compare Bayesian and frequentist outputs responsibly?
  5. What residual generalization risk remains after CV?

5.8 Hints in Layers

  • Hint 1: Build a narrow vertical slice first.
  • Hint 2: Add grouped effects and hierarchical uncertainty.
  • Hint 3: Add forecast and simulation modules.
  • Hint 4: Add cross-model consistency and governance checks.

5.9 Books That Will Help

Topic Book Chapter
Multilevel modeling Gelman & Hill core chapters
Bayesian hierarchy Statistical Rethinking hierarchical chapters
Forecasting practice Forecasting: Principles and Practice advanced chapters

6. Testing Strategy

  • Synthetic recovery tests for grouped structures.
  • Cross-model agreement and sensitivity tests.
  • Scenario stress tests for downside risk.

7. Common Pitfalls & Debugging

Pitfall Symptom Solution
Over-complex models unstable operational recommendations add simpler benchmark baselines
Weak subgroup checks unfair or brittle policy effects subgroup diagnostics and constraints
Conflicting evidence unresolved decision paralysis explicit decision hierarchy and utility function

8. Extensions & Challenges

  • Add online learning updates with drift governance.
  • Add cost-sensitive utility optimization layer.

9. Real-World Connections

  • Pricing and promotion systems.
  • Multi-region policy simulation and planning.

10. Resources

  • Gelman & Hill
  • Statistical Rethinking
  • Forecasting: Principles and Practice

11. Self-Assessment Checklist

  • I can defend model-family choices by data structure.
  • I can reconcile conflicting evidence sources.
  • I can deliver an uncertainty-aware policy packet.

12. Submission / Completion Criteria

Minimum: integrated pipeline with at least two advanced model families.

Full: all modules + simulation + governance-ready final packet.