DATA1002 Chap.5 Data Cleaning, Provenance and Quality

Data Cleaning, Provenance and Quality

Define data provenance

The course material gives this chapter a concrete anchor: The lifecycle, lab and project materials require students to document source, metadata, cleaning and ingestion before reporting summaries.

That data provenance anchor controls how missing-value convention is explained and how data-quality rule is tested in changed practice.

Data Cleaning, Provenance and Quality turns data provenance, missing-value convention and data-quality rule into executable reasoning.

The chapter's practical target is to build a cleaning pipeline whose assumptions and row-level effects can be audited, so every explanation should connect syntax to program state, control flow and observable output.

Treat data provenance as a precise program object, not a loose label. Identify the value or responsibility of data provenance before execution, then trace what can read it, change it or depend on it.

This makes state changes visible before they become debugging guesses.

Use missing-value convention to explain the program's next move. Work through one representative missing-value convention input by hand and name the branch, iteration or call that follows.

If the missing-value convention trace cannot be stated, the code may run by accident rather than by understood design.

Bring in data-quality rule as the test of structure.

Compare normal, boundary and invalid inputs for data-quality rule; state the expected behaviour first; then use the mismatch between expectation and result to localise the defect.

For the application — build a cleaning pipeline whose assumptions and row-level effects can be audited — write the smallest complete example that exposes the rule.

Explain why the data-quality rule result works, what would break it and how the program should signal or recover from that failure.

Trace missing-value convention

Before running an data provenance example, make a trace table with the important state before and after each operation.

Include the value associated with data provenance, the control decision governed by missing-value convention and the output or object affected by data-quality rule. The data provenance table turns an unexplained result into a sequence that can be tested one transition at a time.

Test three inputs: an ordinary case, a boundary case and an invalid case.

State the expected data-quality rule result for each before execution, then compare it with what the program actually does. A useful test of missing-value convention isolates one rule; changing several conditions at once cannot reveal which condition caused the failure.

Practise explaining the solution without reading the code.

For DATA1002, name the data representation, the control flow, the responsibility of each function or class and the reason the chosen design supports build a cleaning pipeline whose assumptions and row-level effects can be audited.

This data-quality rule rehearsal matters when a written test or interview asks why the program works rather than whether it produces one correct output.

A complete response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to missing-value convention, and use data-quality rule to test the result.

The final sentence about data-quality rule should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: Clean-looking data are not necessarily representative, correctly labelled or fit for the intended decision.

Keep that data-quality rule limit beside the worked example, because it separates a careful DATA1002 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve data provenance, missing-value convention and data-quality rule without notes, explain their relationship aloud, then complete a changed version of the application: build a cleaning pipeline whose assumptions and row-level effects can be audited.

Record the first failed missing-value convention reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  data provenance

• 02

  missing-value convention

• 03

  data-quality rule

• 04

  Applying data provenance

• 05

  Limits of missing-value convention and data-quality rule

AskSia practice: apply Data Cleaning, Provenance and Quality

Key terms

data provenance

Recorded information about where data came from and how they were collected, transformed and used. Use this definition when the task is to build a cleaning pipeline whose assumptions and row-level effects can be audited.

missing-value convention

An explicit encoding and interpretation of absent, unknown, inapplicable or withheld data. Use this definition when the task is to build a cleaning pipeline whose assumptions and row-level effects can be audited.

data-quality rule

A testable expectation about validity, consistency, completeness, uniqueness or timeliness of data. Use this definition when the task is to build a cleaning pipeline whose assumptions and row-level effects can be audited.

Data Cleaning, Provenance and Quality FAQ

Exam move

Reconstruct the relationship among data provenance, missing-value convention and data-quality rule; complete the chapter application without notes; then test the result against this limit: Clean-looking data are not necessarily representative, correctly labelled or fit for the intended decision.

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