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Data Science Foundations · Lesson 3

Reproducible Project Structure

Reproducible Project Structure establishes the purpose and structure of a data-science project.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

What Reproducible Project Structure actually means

Reproducible Project Structure establishes the purpose and structure of a data-science project. Data science combines problem framing, data engineering, statistics, computing and modelling to produce reproducible evidence or predictions that support a real decision.

Reproducible Project Structure matters because data science combines problem framing, data, statistics, computation and modelling. Clear purpose and reproducible structure keep the technical work connected to the real prediction, explanation or decision objective.

Deeper walkthrough

Read Reproducible Project Structure as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Translate the real question into an analytical target or estimand. Stage 2: Define the unit of analysis and time of prediction/decision. Stage 3: Specify what information is legitimately available at that time. Final checkpoint: Evaluate using metrics that reflect the decision costs and communicate uncertainty.

Mechanism

Follow the transformation

Translate the real question into an analytical target or estimand.

Define the unit of analysis and time of prediction/decision.

Specify what information is legitimately available at that time.

Evidence

Know what would convince you

  • Write the decision question, unit of analysis and metric formula in plain language before computing it.
  • Reconcile KPI numerators/denominators or grouped totals to source counts for a small slice.
Useful distinctionExplanation: Understand relationships/mechanisms; interpretability and causal design may dominate.
How it works

Trace the mechanism step by step

  1. Translate the real question into an analytical target or estimand.
  2. Define the unit of analysis and time of prediction/decision.
  3. Specify what information is legitimately available at that time.
  4. Build a reproducible pipeline from raw inputs to results.
  5. Evaluate using metrics that reflect the decision costs and communicate uncertainty.
Worked demonstration

Make the concept concrete

Demonstration

Text example

project/
  data/raw/
  data/processed/
  src/
  notebooks/
  tests/
  outputs/
  requirements.txt (or pyproject.toml)
  README.md
Expected / illustrative result
Separate raw data, reusable code, exploratory notebooks, tests and outputs so the workflow can be rerun rather than reconstructed from memory.
Interpret the result.

For Reproducible Project Structure, connect the displayed result to the specific input and mechanism above; independently verify one value/state change rather than treating successful execution as proof.

Distinctions & related ideas

Know what this is — and what it is not

ExplanationUnderstand relationships/mechanisms; interpretability and causal design may dominate.
PredictionAccurately estimate unknown outcomes for new cases.
Causal inferenceEstimate effects of interventions under identification assumptions.
Data analyticsOften emphasises descriptive/diagnostic decision support; overlaps substantially with data science.
Use deliberately

When it is appropriate

Use Reproducible Project Structure when it connects a clearly framed stakeholder question to measurable evidence and an action or decision.

Boundary conditions

When to stop or reconsider

Reframe the analysis if the decision, unit of analysis, metric definition, comparison group or time window is still ambiguous; more computation will not repair an undefined question.

Common mistakes

Failure modes to recognise

  • Starting with a favourite chart/tool before defining the decision and unit of analysis.
  • Using an undefined KPI, denominator, cohort or time window and then comparing incomparable numbers.
  • Turning association or a descriptive pattern into a causal recommendation without supporting design/evidence.
Verification

How to check the result

  • Write the decision question, unit of analysis and metric formula in plain language before computing it.
  • Reconcile KPI numerators/denominators or grouped totals to source counts for a small slice.
  • Test whether the conclusion changes under one reasonable alternative definition or comparison window.
Hands-on practice

Demonstrate understanding

Try this:

Build a tiny, inspectable example of Reproducible Project Structure. First translate the real question into an analytical target or estimand. Then define the unit of analysis and time of prediction/decision. Write the expected result before running it, and explain one condition that would make the result misleading or invalid.

Write the decision, unit and metric definition first. Use a tiny slice where you can recompute the KPI/table manually and explain what would change the recommendation.
Knowledge check

Check reasoning, not memorisation

Before trusting a result from Reproducible Project Structure, which check provides the strongest evidence that you understand and applied it correctly?

Quick reference

Keep the important distinctions visible

Step 1Translate the real question into an analytical target or estimand.
Step 2Define the unit of analysis and time of prediction/decision.
Step 3Specify what information is legitimately available at that time.
Step 4Build a reproducible pipeline from raw inputs to results.
Lesson summary

What to remember

  • Reproducible Project Structure establishes the purpose and structure of a data-science project. Data science combines problem framing, data engineering, statistics, computing and modelling to produce reproducible evidence or predictions that support a real decision.
  • Translate the real question into an analytical target or estimand.
  • Starting with a favourite chart/tool before defining the decision and unit of analysis.
  • Write the decision question, unit of analysis and metric formula in plain language before computing it.