← ACARA Digital Technologies Years 9–10

The data science life cycle

A repeating cycle of five stages: define the objective, acquire data, clean it, analyse it, communicate insights. Findings can send you back to an earlier stage.

Part of: ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data — Module 2, ACARA Digital Technologies Years 9–10 — Analysing and Visualising Data — Module 2

Understanding The data science life cycle in ACARA Digital Technologies Years 9–10

What it does: This sequence lists the five stages of the data science life cycle in the correct order. Findings often raise new questions, so the last stage loops back to the first and the process repeats. The cycle starts by defining the objective. Then you acquire data, clean it, analyse it and communicate your insights. Because the life cycle repeats, you can return to an earlier stage when you find a problem. Skipping ahead would give unreliable insights. Communicating insights is the final stage. It is where you present your findings, such as in charts and reports, to others. Removing duplicates and fixing spelling are data cleaning tasks. They prepare the data so the analysis can be trusted.

Common mistakes: The second stage of the life cycle is acquiring data. Cleaning comes third, so stage2 should be "acquire data". The life cycle has five stages, so the loop must run from 1 TO 5. Stopping at 4 skips the last stage, which is communicating insights.

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This module's key terms are also in a printable crossword (Modules 226–254) and word search (Modules 226–329).

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Learning

Duo

Which list shows the five stages of the data science life cycle in the correct order?

Define objective, acquire data, clean, analyse, communicateAcquire data, analyse, define objective, communicate, clean

Matches

analyse -> acquire -> define -> clean -> communicate

↓

The stages are out of order: you cannot analyse data before you have defined the objective, acquired the data and cleaned it.

Applying

Meanings

Term

data science life cycle

Meaning

A repeating cycle of five stages: define the objective, acquire data, clean it, analyse it, communicate insights. Findings can send you back to an earlier stage.

Practice

REPEAT

Testing

Gaps

Define -> -> Clean ->

Check Answer

Spots

1stage1 ← "define objective"

2stage2 ← "clean data"

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Quick Questions

1. Which list shows the five stages of the data science life cycle in the correct order?

A. Define objective, acquire data, clean, analyse, communicate ✓

B. Acquire data, analyse, define objective, communicate, clean

The cycle starts by defining the objective. Then you acquire data, clean it, analyse it and communicate your insights.

2. What is the very first stage of the data science life cycle?

A. Acquire data

B. Define the objective ✓

You need to know what question you are answering before you collect any data, so defining the objective comes first.

3. The data science life cycle is best described as...

A. A straight line that you only follow once

B. A repeating cycle that can be revisited ✓

It is a cycle, not a line. Findings often raise new questions or show problems, which sends you back to an earlier stage.

4. You start analysing your data and find many missing values. What is the best next step in the life cycle?

A. Go back to the clean stage and fix them ✓

B. Skip ahead and communicate the results anyway

Because the life cycle repeats, you can return to an earlier stage when you find a problem. Skipping ahead would give unreliable insights.

5. Which stage involves making charts and sharing what you found with an audience?

A. Communicate insights ✓

B. Acquire data

Communicating insights is the final stage. It is where you present your findings, such as in charts and reports, to others.

Match the Pairs

Stage 1: Define the objective → Decide the question you want to answer before touching any data

Stage 3: Clean the data → Fix errors, remove duplicates and handle missing values so the data can be trusted

The life cycle has five stages in order, from objective to insights → define -> acquire -> clean -> analyse -> communicate

It is a cycle, not a line: communicating insights can lead to a new question → communicate -> define (repeat)

analyse -> acquire -> define -> clean -> communicate → The stages are out of order: you cannot analyse data before you have defined the objective, acquired the data and cleaned it.

Life cycle: define -> acquire -> clean -> analyse -> communicate -> END (never go back) → Treats the life cycle as a one-way line. Findings can send you back to an earlier stage, so the process repeats.

Key Term

data science life cycle

A repeating cycle of five stages: define the objective, acquire data, clean it, analyse it, communicate insights. Findings can send you back to an earlier stage.

Fill in the Blanks

1. Define -> Acquire -> Clean -> Analyse -> Communicate

Hint: Type the second stage, where you collect or get the data you need.

2. Define -> Acquire -> Clean -> Analyse -> Communicate

Hint: Type the stage where you fix errors, duplicates and missing values before analysing.

3. Analyse -> Communicate -> Define

Hint: Type the stage the cycle returns to after insights are communicated, which starts the next loop.

4. REPEAT runFiveStages() UNTIL questionAnswered

Hint: Type the loop keyword that shows the stages are run again, making a cycle instead of a straight line.

5. IF needsMoreData THEN GO BACK TO Acquire END IF

Hint: Type the keyword that follows the condition and starts the action to take when a finding sends you back.

Spot the Error

Question 1

A stage1 ← "define objective"

B stage2 ← "clean data"

C stage3 ← "clean data"

D stage4 ← "analyse data"

E stage5 ← "communicate"

Line B — The second stage of the life cycle is acquiring data. Cleaning comes third, so stage2 should be "acquire data".

Question 2

A FOR stage ← 1 TO 4

B DISPLAY stages[stage]

C NEXT stage

Line A — The life cycle has five stages, so the loop must run from 1 TO 5. Stopping at 4 skips the last stage, which is communicating insights.

Question 3

A analyse_data()

B acquire_data()

C clean_data()

D analyse_data()

Line A — The cycle starts by defining the objective. Line 1 should be define_objective(). You cannot analyse data before you know what question you are answering.

Question 4

A communicate_insights()

B IF new_question THEN

C STOP

D ENDIF

Line C — The life cycle repeats. When communicating raises a new question, the program should go back to define_objective instead of stopping.

Question 5

A analyse_data()

B IF results_look_wrong THEN

C check_data_quality()

D GOTO communicate_insights

E ENDIF

Line D — If the results look wrong, you should return to an earlier stage such as clean_data to fix the problem. Communicating wrong results would mislead the audience.

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