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Capstone Analytics Project · Lesson 90

Perform EDA

Capstone EDA systematically examines distributions, groups, time patterns, relationships and anomalies that bear on the business question.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Perform EDA

Capstone EDA systematically examines distributions, groups, time patterns, relationships and anomalies that bear on the business question.

Learning goal: explain why Perform EDA behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims.

Deeper walkthrough

Read Perform EDA as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims. 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

Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims.

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

Begin with schema/quality

Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims.

Verification focus: record the evidence you inspected and the condition that would make this stage fail.
How it works

Trace the mechanism step by step

  1. Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims.
  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

Perform EDA evidence

Perform EDA evidence
Evidence: a small set of annotated findings tied directly to the decision question.
Expected / illustrative result
The worked evidence makes the output of this project stage concrete and auditable.
Interpret the result.

For Perform EDA, 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 Perform EDA 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 Perform EDA 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 Perform EDA. First begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims. 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 Perform EDA?

Quick reference

Remember the logic

Step 1Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims.
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

  • Capstone EDA systematically examines distributions, groups, time patterns, relationships and anomalies that bear on the business question.
  • Begin with schema/quality, then univariate distributions, group/time comparisons and relationships; turn observations into testable questions rather than causal claims.
  • Changing the population/grain without noticing it.
  • Recompute one result from a handful of source rows or an independent formula.