← ACARA Digital Technologies Years 9–10

Applying the data science process to a scenario

Work through a scenario in stages: define the objective, acquire data, validate and clean it, analyse it, report insights, and go back a stage if the data is poor.

Part of: ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data — Module 121

Understanding Applying the data science process to a scenario in ACARA Digital Technologies Years 9–10

What it does: Acquiring data from a repository means downloading a file and filtering it. This is the second stage of the process. You must know what you are trying to find out before you collect or chart anything. Defining the objective always comes first. An age of 150 is not reasonable. Errors like this are caught in the validate and clean stage so they do not distort the analysis. A sensor records measurable, objective data over time. A single guess is unreliable and does not give enough data. -999 is a typical error code for a faulty sensor. Leaving it in would make the mean far too low.

Common mistakes: The process starts by defining the objective, so line 1 must be the define stage. Reporting insights is the last stage. It should read: define: how much do we sleep? In a sensor scenario the acquire stage collects real readings from the sensor, not guesses. It should read: acquire: log temp every 10 min.

Class: _____________________  ·  Name: _____________________  ·  Date: _________

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This module's key terms are also in a printable crossword (Modules 333–377) and word search (Modules 333–412).

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Learning
Duo
A Year 9 class wants to know if students sleep enough on school nights. Which stage comes FIRST in the data science process?
Define the objective (the research question)Draw a chart of the results
Matches
Survey scenario: analyse the results first, then write the research question after seeing the answers.
↓
The stages are in the wrong order. The objective must be defined before data is collected or analysed.
Applying
Meanings
Term
data science process
Meaning
Work through a scenario in stages: define the objective, acquire data, validate and clean it, analyse it, report insights, and go back a stage if the data is poor.
Practice
DEFINE objective: find the hottest day
Testing
Gaps
("Do teens sleep enough?")
Check Answer
Spots
1report: how much do we sleep?
2acquire: survey 30 students

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Quick Questions

1. A Year 9 class wants to know if students sleep enough on school nights. Which stage comes FIRST in the data science process?
A. Define the objective (the research question) ✓
B. Draw a chart of the results
You must know what you are trying to find out before you collect or chart anything. Defining the objective always comes first.
2. Survey scenario: students have finished filling in an online survey about screen time. What is the next stage?
A. Validate and clean the responses ✓
B. Report insights to the principal
After acquiring data you check it for errors, blanks and duplicates (validate and clean) before you analyse it. Reporting is the last stage.
3. Survey scenario: 12 responses say the student is 150 years old. What should you do?
A. Flag them as invalid during cleaning and decide whether to fix or remove them ✓
B. Keep them because every response counts equally
An age of 150 is not reasonable. Errors like this are caught in the validate and clean stage so they do not distort the analysis.
4. Sensor scenario: you want to know if the school oval is too hot at lunch. Which is the best way to ACQUIRE the data?
A. Ask one friend to guess the temperature
B. Log readings from a temperature sensor at regular intervals ✓
A sensor records measurable, objective data over time. A single guess is unreliable and does not give enough data.
5. Sensor scenario: the temperature log shows -999 for several readings. Which action is correct?
A. Include them in the average because the sensor recorded them
B. Treat them as sensor faults, then clean or remove them before analysing ✓
-999 is a typical error code for a faulty sensor. Leaving it in would make the mean far too low.

Match the Pairs

Stage 1: Define the objective → Write a clear research question first, such as 'Do Year 9 students who sleep more score higher in tests?', so you know what data to collect.
Stage 3: Validate and clean → Check each reading or response against rules and fix errors, for example removing a sensor reading of -999 or a survey age of 250.
Repository scenario: acquire data by downloading a CSV from an open data portal, then check its licence and filter it to the rows you need. → Stage 2: Acquire -> download rainfall.csv, filter Year = 2024
Going back a stage: validation shows many sensor readings are missing, so return to the acquire stage and collect more data before analysing. → IF missing_readings > 30% THEN go back to Stage 2: Acquire
Survey scenario: analyse the results first, then write the research question after seeing the answers. → The stages are in the wrong order. The objective must be defined before data is collected or analysed.
Sensor scenario: the temperature log has errors, but we skip cleaning and report the average anyway. → Skipping validation and cleaning means faulty readings distort the average, so the insights reported are unreliable.

Key Term

data science process
Work through a scenario in stages: define the objective, acquire data, validate and clean it, analyse it, report insights, and go back a stage if the data is poor.

Fill in the Blanks

1. define("Do teens sleep enough?") data ← acquire("survey")
Hint: Type the name of the first stage, where you set the objective or question.
2. data ← acquire("temperature sensor") data ← validate(data) analyse(data)
Hint: Type the stage that checks sensor readings are acceptable before they are analysed.
3. data ← acquire("open data portal") data ← validate(data) data ← clean(data)
Hint: Type the stage that fixes duplicates, spelling variations and missing values.
4. IF quality = "poor" THEN GO BACK TO acquire ENDIF
Hint: Type the stage you return to when you need to collect better data.
5. insights ← analyse(data) report(insights)
Hint: Type the final stage, where findings are shared with an audience.

Spot the Error

Question 1
A report: how much do we sleep?
B acquire: survey 30 students
C validate: ages must be 13-16
D report: share the insight
Line A — The process starts by defining the objective, so line 1 must be the define stage. Reporting insights is the last stage. It should read: define: how much do we sleep?
Question 2
A define: is the room too hot?
B acquire: guess the temps
C validate: drop faulty -99s
D analyse: find the average
Line B — In a sensor scenario the acquire stage collects real readings from the sensor, not guesses. It should read: acquire: log temp every 10 min
Question 3
A define: do cities get hotter?
B analyse: download open CSV
C clean: remove duplicate rows
D analyse: compare yearly means
Line B — Downloading data from a repository is the acquire stage, not the analyse stage. Analysis happens after the data is acquired and cleaned. It should read: acquire: download open CSV
Question 4
A if too many errors: analyse
B acquire: collect data again
C validate: check the new data
Line A — When data is poor you must not analyse it. You go back a stage, such as acquiring better data. It should read: if too many errors: go back
Question 5
A define: best canteen food?
B acquire: survey 50 students
C clean: merge Pizza and pizza
D analyse: ignore the answers
E report: show a bar chart
Line D — The analyse stage works with the cleaned data to find patterns, such as counting each answer. Ignoring the answers would make the survey pointless. It should read: analyse: count each answer

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