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Capstone Analytics Project · Lesson 89

Audit and Clean the Dataset

The capstone data audit verifies row grain, keys, types, missingness, duplicates, categories and plausible ranges before any headline metric is trusted.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Audit and Clean the Dataset

The capstone data audit verifies row grain, keys, types, missingness, duplicates, categories and plausible ranges before any headline metric is trusted.

Learning goal: explain why Audit and Clean the Dataset behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Create a compact quality table, fix only justified issues and record what was changed or excluded.

Deeper walkthrough

Read Audit and Clean the Dataset as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Create a compact quality table, fix only justified issues and record what was changed or excluded. 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

Create a compact quality table, fix only justified issues and record what was changed or excluded.

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

  • Recompute one result from a handful of source rows or an independent formula.
  • Check row counts, group totals and units before interpreting differences.
Useful distinctionDefinition: The exact metric/selection/comparison being computed.
Click a stage to inspect what happens, what changes, and what should be checked before moving on.
Stage 1

Create a compact quality table

Create a compact quality table, fix only justified issues and record what was changed or excluded. For Audit and Clean the Dataset, identify the exact state before this stage, the operation or rule applied here, and the observable state afterwards so the mechanism remains inspectable.

State focus: identify exactly what changed at this stage and what observable evidence confirms that change.
How it works

Trace the mechanism step by step

  1. Create a compact quality table, fix only justified issues and record what was changed or excluded.
  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

Audit and Clean the Dataset evidence

Audit and Clean the Dataset evidence
Evidence: before/after row counts and a list of cleaning rules with their affected counts.
Expected / illustrative result
The worked evidence makes the output of this project stage concrete and auditable.
Interpret the result.

For Audit and Clean the Dataset, 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

DefinitionThe exact metric/selection/comparison being computed.
EvidenceTable, formula or visual that answers the question.
AuditIndependent count/total/rule check that can reveal an error.
Use deliberately

When it is appropriate

Use Audit and Clean the Dataset when it answers a defined question in Capstone Analytics Project and its inputs/assumptions match the current data or program state.

Boundary conditions

When to stop or reconsider

Reconsider Audit and Clean the Dataset 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

  • Changing the population/grain without noticing it.
  • Using an undefined denominator, time window, unit or category rule.
  • Presenting a number/plot without reconciling it to source counts or totals.
Verification

How to check the result

  • Recompute one result from a handful of source rows or an independent formula.
  • Check row counts, group totals and units before interpreting differences.
  • Change one source value and predict which reported value/mark should change.
Hands-on practice

Demonstrate understanding

Try this:

Construct a tiny example of Audit and Clean the Dataset. First create a compact quality table, fix only justified issues and record what was changed or excluded. 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 Audit and Clean the Dataset?

Quick reference

Remember the logic

Step 1Create a compact quality table, fix only justified issues and record what was changed or excluded.
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

  • The capstone data audit verifies row grain, keys, types, missingness, duplicates, categories and plausible ranges before any headline metric is trusted.
  • Create a compact quality table, fix only justified issues and record what was changed or excluded.
  • Changing the population/grain without noticing it.
  • Recompute one result from a handful of source rows or an independent formula.