Year 9 · Spring 1

Data Science and AI · Lesson 4 of 5

Visualising; correlation vs causation

TheoryTopic 18 Data, spreadsheets and AI

Year 9 · Data Science and AI · Lesson 4 of 5

You will learn to

  • Choose a bar, line, pie or scatter chart to fit a question, and explain the choice.
  • Make a scatter chart of two columns and describe the pattern: positive, negative or none.
  • Explain why a correlation does not prove that one thing causes the other.
  • Identify a hidden third factor, like hot sunny weather behind ice cream sales and sunburn.

Year 9 · Data Science and AI · Lesson 4 of 5

Success criteria

You have got it when you can say:

  • I can make a scatter chart with both axes labelled.
  • I can describe a correlation in one sentence: 'As … goes up, … goes up (or down).'
  • I can explain the ice cream and sunburn link without saying one causes the other.

Year 9 · Data Science and AI · Lesson 4 of 5

Retrieval starter

Questions from before. Write your answers, then show them.

  1. Last lesson

    To show change over time, use a…

    A Pie chartB Line chartC Scatter graph

    B. Line chart

  2. Last unit

    A fake bank email asking you to log in is…

    A A wormB PhishingC Brute force

    B. Phishing

  3. Earlier this year

    The program runs but gives the wrong answer. That's a…

    A Syntax errorB Runtime errorC Logic error

    C. Logic error

Year 9 · Data Science and AI · Lesson 4 of 5

Key words (1 of 2)

Formula
A spreadsheet calculation. Always starts with =.
Function
A subroutine that returns a value.
Record / field
One row in a database table: everything stored about one thing.
Primary key
A field whose value is unique for every record.

Year 9 · Data Science and AI · Lesson 4 of 5

Key words (2 of 2)

SQL
Structured Query Language: asks databases questions.
Machine learning
The computer finds rules itself from labelled examples.

The explanation · step 1 of 1

Charts

ChartUse it forExample
BarComparing categoriesVotes per club
LineChange over timeTemperature each hour
PieParts of one wholeHow you spend 24 hours
ScatterIs there a link?Revision hours vs score

Year 9 · Data Science and AI · Lesson 4 of 5

Quick quiz · 1 of 3

A face-recognition model works badly on faces unlike its training photos. This is…

  1. A A syntax error
  2. B Overflow
  3. C Bias from unrepresentative training data

C. Bias from unrepresentative training data

The model only learns what it is shown. Missing groups in the data means worse results for those groups.

Year 9 · Data Science and AI · Lesson 4 of 5

Quick quiz · 2 of 3

Every spreadsheet formula starts with…

  1. A #
  2. B $
  3. C =

C. =

The = tells the spreadsheet: calculate this, don't just show it.

Year 9 · Data Science and AI · Lesson 4 of 5

Quick quiz · 3 of 3

=B2*C2 in D2 is filled down to D5. What is in D5?

  1. A =B5*C5
  2. B =B2*C2
  3. C =E2*F2

A. =B5*C5

Relative references move with the formula: 3 rows down, so 2 becomes 5.

Year 9 · Data Science and AI · Lesson 4 of 5

Exit ticket

Answer on paper before you go. Then check.

  1. In Spanish Excel, =IF(B2>=25,"Pass","Retry") becomes…

    A =SI(B2>=25,"Aprobado","Repetir")B =SI(B2>=25;"Aprobado";"Repetir")C =IF(B2>=25;"Pass";"Retry")

    B. =SI(B2>=25;"Aprobado";"Repetir") Spanish name SI, and semicolons, because the comma is the decimal point.

  2. The numbers 3, 9, 4, 4, 10. What is the median?

    A 9B 6C 4

    C. 4 Sort: 3, 4, 4, 9, 10. The middle (3rd) value is 4.