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Capstone: A Small Data Project · Lesson 159

Plan a Small Data Project

Planning turns a vague programming idea into a bounded workflow with a defined input, output, success criterion and folder structure before code is written.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Plan a Small Data Project

Planning turns a vague programming idea into a bounded workflow with a defined input, output, success criterion and folder structure before code is written.

Learning goal: explain why Plan a Small Data Project behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed.

Deeper walkthrough

Read Plan a Small Data Project as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed. Stage 2: Keep the stage inside the same data/validation definitions used by the rest of the project. Stage 3: Save the evidence produced by this stage so the next stage can be audited.

Mechanism

Follow the transformation

Write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed.

Keep the stage inside the same data/validation definitions used by the rest of the project.

Save the evidence produced by this stage so the next stage can be audited.

Evidence

Know what would convince you

  • Trace a tiny input by hand and compare the runtime result.
  • Inspect type, value/shape and any mutation/side effect explicitly.
Useful distinctionInput: Objects/values supplied to the operation.
How it works

Trace the mechanism step by step

  1. Write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed.
  2. Keep the stage inside the same data/validation definitions used by the rest of the project.
  3. Save the evidence produced by this stage so the next stage can be audited.
Worked demonstration

Plan a Small Data Project evidence

Plan a Small Data Project evidence
Plan example: input sales.csv → validate required columns → compute monthly totals → save summary.csv + plot.png.
Expected / illustrative result
The worked evidence makes the output of this project stage concrete and auditable.
Interpret the result.

For Plan a Small Data Project, trace the specific input through the mechanism above and independently verify one returned value, state change or side effect.

Distinctions & related ideas

Place the concept correctly

InputObjects/values supplied to the operation.
StateNames or mutable objects that may change during execution.
OutputReturned value, side effect, file, plot or exception to inspect.
Use deliberately

When it is appropriate

Use Plan a Small Data Project when it answers a defined question in Capstone: A Small Data Project and its inputs/assumptions match the current data or program state.

Boundary conditions

When to stop or reconsider

Reconsider Plan a Small Data Project when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.

Common mistakes

Failure modes to recognise

  • Running the operation on the wrong object/type or in the wrong environment.
  • Inferring correctness from “no exception” without checking the produced value/state.
  • Hiding a boundary case instead of making its behaviour explicit.
Verification

How to check the result

  • Trace a tiny input by hand and compare the runtime result.
  • Inspect type, value/shape and any mutation/side effect explicitly.
  • Run an edge or invalid case and confirm the exception/behaviour is deliberate.
Hands-on practice

Demonstrate understanding

Try this:

Construct a tiny example of Plan a Small Data Project. First write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed. Then keep the stage inside the same data/validation definitions used by the rest of the project. Predict the result before execution and explain one boundary or failure case.

List the stage inputs and expected artifact, rerun it from a clean state, and compare against a concrete acceptance check.
Knowledge check

Check reasoning, not memorisation

Which approach best demonstrates understanding of Plan a Small Data Project?

Quick reference

Remember the logic

Step 1Write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed.
Step 2Keep the stage inside the same data/validation definitions used by the rest of the project.
Step 3Save the evidence produced by this stage so the next stage can be audited.
Lesson summary

What to remember

  • Planning turns a vague programming idea into a bounded workflow with a defined input, output, success criterion and folder structure before code is written.
  • Write a one-paragraph problem statement, identify input file(s), define the output artifact, list validation checks and sketch the functions/modules needed.
  • Running the operation on the wrong object/type or in the wrong environment.
  • Trace a tiny input by hand and compare the runtime result.