Project 15: Practical Data Competence Pipeline

Build a reproducible analytics pipeline from messy input data to a decision-ready memo.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 1-2 weeks
Main Programming Language Python + SQL
Alternative Programming Languages R, dbt-style workflows
Coolness Level Level 3: Genuinely Clever
Business Potential 3. Service & Support
Prerequisites Projects 8-11
Key Topics Cleaning, missing data, feature engineering, visualization, reproducibility, communication

1. Learning Objectives

  1. Enforce data contracts and quality gates.
  2. Handle missingness with explicit mechanism assumptions.
  3. Build leakage-safe feature engineering.
  4. Deliver reproducible, stakeholder-friendly outputs.

2. All Theory Needed (Per-Concept Breakdown)

2.1 Data Quality and Missingness

  • Fundamentals: Statistical validity starts with data validity.
  • Deep Dive into the concept: Missingness mechanism assumptions (MCAR/MAR/MNAR) determine which handling strategies are defensible.

2.2 Reproducibility and Communication

  • Fundamentals: If results cannot be rerun and explained, they are operationally weak.
  • Deep Dive into the concept: Run manifests, artifact hashes, and uncertainty-aware summaries create trust.

3. Project Specification

3.1 What You Will Build

A pipeline that ingests noisy data, validates schema and quality, handles missing data, engineers features, and publishes an executive memo.

3.2 Functional Requirements

  1. Schema/type/range validation module.
  2. Missingness report + strategy decision log.
  3. Feature engineering with leakage checks.
  4. Reproducibility package (configs, hashes, outputs).

3.3 Non-Functional Requirements

  • Deterministic reruns with fixed seeds.
  • Clear assumptions manifest.

3.4 Example Usage / Output

$ python practical_pipeline.py --run weekly_kpi
Schema violations fixed: 1402
Missingness strategy: MAR-impute on 6 fields, drop on 2
Feature checks: 18 passed, 1 warning
Reproducibility hash: 6fa2a9d1
Executive memo: outputs/practical_pipeline/weekly_kpi_brief.md

3.5 Real World Outcome

You deliver an auditable analysis package that another analyst can rerun and defend.

4. Solution Architecture

Raw input -> quality gate -> missingness module -> feature pipeline -> report + artifact bundle

5. Implementation Guide

5.1 Development Environment Setup

pip install pandas numpy duckdb

5.2 Project Structure

P15/
  practical_pipeline.py
  checks/
  outputs/
  manifests/

5.3 The Core Question You Are Answering

“Can this analysis be rerun, audited, and understood by others without hidden notebook state?”

5.4 Concepts You Must Understand First

  1. Data quality dimensions
  2. Missingness mechanisms
  3. Leakage and temporal cutoff rules
  4. Communication for non-technical audiences

5.5 Questions to Guide Your Design

  1. What quality failures block execution?
  2. How will you justify imputation choices?
  3. How will you represent uncertainty in executive summaries?

5.6 Thinking Exercise

Write two summaries of the same result: one technical and one executive. Compare language and detail.

5.7 The Interview Questions They’ll Ask

  1. What is target leakage and how do you detect it?
  2. How do you decide between drop vs impute?
  3. What does reproducibility mean in practice?
  4. How do you communicate uncertainty responsibly?
  5. What belongs in a run manifest?

5.8 Hints in Layers

  • Hint 1: Build validation gates first.
  • Hint 2: Add missingness strategy logging.
  • Hint 3: Add leakage checks in feature pipeline.
  • Hint 4: Add deterministic artifact packaging.

5.9 Books That Will Help

Topic Book Chapter
Data workflow The Art of Data Science full text
Reproducibility R for Data Science workflow chapters
Communication data storytelling references selected

6. Testing Strategy

  • Schema-failure tests.
  • Missingness injection tests.
  • Rerun hash consistency tests.

7. Common Pitfalls & Debugging

Pitfall Symptom Solution
Hidden notebook state irreproducible outputs scriptable step pipeline
Untracked imputations silent bias drift mandatory strategy logs
Weak communication stakeholder confusion structured memo template

8. Extensions & Challenges

  • Add CI checks for data contracts.
  • Add visualization linting rules.

9. Real-World Connections

  • Analytics engineering foundations.
  • Audit-ready KPI reporting.

10. Resources

  • The Art of Data Science
  • R for Data Science

11. Self-Assessment Checklist

  • I can rerun end-to-end and reproduce outputs.
  • I can defend missing-data decisions.
  • I can communicate uncertainty to decision-makers.

12. Submission / Completion Criteria

Minimum: deterministic pipeline with quality gates and final memo.

Full: includes artifact bundle, manifests, and communication variants.