← VCE Data Analytics U3O1

Purpose of a data visualisation: exploratory data analysis

Using quick charts and summaries to discover patterns, relationships and outliers in data. The analyst is the audience, trying different charts to form questions.

Part of: VCE Data Analytics - U3O1 - Data Analytics — Module 98

Understanding Purpose of a data visualisation: exploratory data analysis in VCE Data Analytics U3O1

What it does: Finding the range is a quick exploratory summary that shows how spread out the values are. COUNTIF gives a fast count that helps the analyst notice patterns and form questions about the data. In exploratory analysis the analyst is the audience. The charts are working tools to help them understand the data, not finished products for others. Exploring is about finding out what is in the data. Presenting a finished message is a different purpose. Exploratory analysis involves quickly trying several chart types, because you often do not know which one will reveal a pattern until you look. A scatter graph shows how two numeric variables relate and can reveal clusters and outliers. A pie chart shows parts of a whole.

Common mistakes: The closing bracket is missing, so the formula will not run. It should be =AVERAGE(B2:B9). Quick summaries like min, max and average are an early step in exploring data. The two ranges are different sizes (B2:B8 and D2:D9), so CORREL gives an error. Both ranges must match: =CORREL(B2:B8,D2:D8). This tests the analyst's question about a relationship.

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This module's key terms are also in a printable crossword (Modules 94–128) and word search (Modules 94–148).

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Learning

Duo

Who is the main audience of a visualisation made for exploratory data analysis?

The analyst who is exploring the dataA client who needs a polished final report

Matches

Exploratory chart: polish colours and fonts for the final audience before looking at the data

↓

Exploratory charts are quick and rough, and the analyst is the audience. Polishing them for others belongs to presenting, not exploring.

Applying

Meanings

Term

exploratory data analysis

Meaning

Using quick charts and summaries to discover patterns, relationships and outliers in data. The analyst is the audience, trying different charts to form questions.

Practice

=ROUND(CORREL(B2:B9,D2:D9),2)

Testing

Gaps

=(B2:B9,C2:C9)

Check Answer

Spots

2=MAX(B2:B9)

3=AVERAGE(B2:B9

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Quick Questions

1. Who is the main audience of a visualisation made for exploratory data analysis?

A. The analyst who is exploring the data ✓

B. A client who needs a polished final report

In exploratory analysis the analyst is the audience. The charts are working tools to help them understand the data, not finished products for others.

2. Which is the main purpose of exploratory data analysis?

A. Discover patterns, relationships and outliers ✓

B. Present a final, polished message to the public

Exploring is about finding out what is in the data. Presenting a finished message is a different purpose.

3. An analyst draws a scatter graph, then a histogram, then a column chart of the same data to see what each shows. What is this called?

A. Choosing the one correct chart before starting

B. Trying different charts to explore the data ✓

Exploratory analysis involves quickly trying several chart types, because you often do not know which one will reveal a pattern until you look.

4. Which chart is a good quick check for a possible relationship between hours studied and test score?

A. A scatter graph of hours against score ✓

B. A pie chart of the hours studied

A scatter graph shows how two numeric variables relate and can reveal clusters and outliers. A pie chart shows parts of a whole.

5. After a quick chart shows one point far from all the others, what should the analyst do next?

A. Delete it straight away without checking

B. Form a question, such as whether it is an error or a real value ✓

Exploration leads to questions. An outlier might be a data entry error or a genuine unusual result, so the analyst investigates before deciding.

Match the Pairs

=CORREL(B2:B9,C2:C9) → A quick summary check to see whether two columns are related, which can give an analyst a question to explore

Insert > Scatter graph (Maths vs English) → Trying a quick chart to see patterns, relationships and outliers, with the analyst as the only audience

Exploring: the analyst tries a quick summary of a column to see how the values are spread before deciding what to ask → =MAX(B2:B9)-MIN(B2:B9)

Exploring: the analyst counts how many values fall in a group to look for a pattern or an unusual value → =COUNTIF(B2:B9,">=80")

Exploratory chart: polish colours and fonts for the final audience before looking at the data → Exploratory charts are quick and rough, and the analyst is the audience. Polishing them for others belongs to presenting, not exploring.

Make one chart, draw a conclusion and stop exploring → Exploring means trying several different charts and summaries to form questions. One chart is not enough to find patterns, relationships or outliers.

Key Term

exploratory data analysis

Using quick charts and summaries to discover patterns, relationships and outliers in data. The analyst is the audience, trying different charts to form questions.

Fill in the Blanks

1. =CORREL(B2:B9,C2:C9)

Hint: Type the function that measures how strongly two columns are related, to explore a possible relationship.

2. =MAX(B2:B9)-MIN(B2:B9)

Hint: Type the function that finds the highest value, so you can check the spread of the data for outliers.

3. SELECT AVG(mark) FROM results;

Hint: Type the aggregate function that gives a quick summary of the typical mark.

4. SELECT * FROM results ORDER BY mark DESC;

Hint: Type the column to sort by so the highest values, possible outliers, appear first.

5. =COUNTIF(B2:B9,"<50")

Hint: Type the comparison operator that counts values below a cut-off, to explore how many low marks there are.

Spot the Error

Question 1

A =MIN(B2:B9)

B =MAX(B2:B9)

C =AVERAGE(B2:B9

Line C — The closing bracket is missing, so the formula will not run. It should be =AVERAGE(B2:B9). Quick summaries like min, max and average are an early step in exploring data.

Question 2

A Q: Does heat affect rain?

B =CORREL(B2:B8,D2:D9)

C Chart: scatter of Max, Rain

Line B — The two ranges are different sizes (B2:B8 and D2:D9), so CORREL gives an error. Both ranges must match: =CORREL(B2:B8,D2:D8). This tests the analyst's question about a relationship.

Question 3

A Step 1: Ask a question

B Step 2: Try a quick chart

C Step 3: Look for patterns

D Step 4: Publish final poster

Line D — Exploratory analysis is a loop: what the analyst finds leads to new questions. Publishing a final poster is presentation, not exploration. The line should be 'Step 4: Ask a new question'.

Question 4

A Audience: the public

B Purpose: explore the data

C Output: quick draft charts

Line A — In exploratory data analysis the analyst is the audience, because they are looking for patterns for themselves. The public is the audience for a presentation visualisation. It should be 'Audience: the analyst'.

Question 5

A SELECT house, AVG(points)

B FROM students

C GROUP house;

Line C — The SQL keyword is GROUP BY, not GROUP. The correct line is GROUP BY house; and it summarises points for each house so the analyst can compare them.

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