DATA1002 Chap.1 Data Questions, Lifecycle and Project Failure

Data Questions, Lifecycle and Project Failure

Define data science lifecycle

The course material gives this chapter a concrete anchor: The current introduction distinguishes data-science work from a linear software project and places iteration, evidence and decision use before modelling.

That data science lifecycle anchor controls how decision question is explained and how project failure mode is tested in changed practice.

Data Questions, Lifecycle and Project Failure turns data science lifecycle, decision question and project failure mode into executable reasoning.

The chapter's practical target is to turn a broad request into a decision question and map the data work needed to answer it, so every explanation should connect syntax to program state, control flow and observable output.

Treat data science lifecycle as a precise program object, not a loose label.

Identify the value or responsibility of data science lifecycle 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 decision question to explain the program's next move. Work through one representative decision question input by hand and name the branch, iteration or call that follows.

If the decision question trace cannot be stated, the code may run by accident rather than by understood design.

Bring in project failure mode as the test of structure.

Compare normal, boundary and invalid inputs for project failure mode; state the expected behaviour first; then use the mismatch between expectation and result to localise the defect.

For the application — turn a broad request into a decision question and map the data work needed to answer it — write the smallest complete example that exposes the rule.

Explain why the project failure mode result works, what would break it and how the program should signal or recover from that failure.

Trace decision question

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

Include the value associated with data science lifecycle, the control decision governed by decision question and the output or object affected by project failure mode. The data science lifecycle 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 project failure mode result for each before execution, then compare it with what the program actually does. A useful test of decision question 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 turn a broad request into a decision question and map the data work needed to answer it.

This project failure mode 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 decision question, and use project failure mode to test the result.

The final sentence about project failure mode should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: A technically correct model can still fail when the question, stakeholders, data provenance or implementation path are wrong.

Keep that project failure mode 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 science lifecycle, decision question and project failure mode without notes, explain their relationship aloud, then complete a changed version of the application: turn a broad request into a decision question and map the data work needed to answer it.

Record the first failed decision question reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  data science lifecycle

• 02

  decision question

• 03

  project failure mode

• 04

  Applying data science lifecycle

• 05

  Limits of decision question and project failure mode

AskSia practice: apply Data Questions, Lifecycle and Project Failure

Key terms

data science lifecycle

An iterative sequence for question framing, data acquisition, cleaning, analysis, modelling, communication and review. Use this definition when the task is to turn a broad request into a decision question and map the data work needed to answer it.

decision question

A bounded question identifying the actor, outcome, evidence and action a data analysis must support. Use this definition when the task is to turn a broad request into a decision question and map the data work needed to answer it.

project failure mode

A recurring condition that prevents a data project from producing valid, usable or maintainable results. Use this definition when the task is to turn a broad request into a decision question and map the data work needed to answer it.

Data Questions, Lifecycle and Project Failure FAQ

Exam move

Reconstruct the relationship among data science lifecycle, decision question and project failure mode; complete the chapter application without notes; then test the result against this limit: A technically correct model can still fail when the question, stakeholders, data provenance or implementation path are wrong.

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