Using Python Lists to Analyse Data Sets

Python is one of the most powerful tools for data analysis — and it all starts with the humble list. Lists allow students to store, sort, and process data efficiently, turning raw numbers into meaningful results. This simple programming concept introduces key computational thinking skills that underpin data science, statistics, and AI.

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The Concept

A list in Python is a collection of data items stored under one variable name. Lists can hold numbers, text, or even other lists.

Example:

data = [12, 15, 18, 20, 22, 25]

From here, students can calculate averages, find maximum and minimum values, or even visualise data using libraries such as matplotlib.

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The Experiment in Code

data = [12, 15, 18, 20, 22, 25]

mean = sum(data) / len(data)
max_value = max(data)
min_value = min(data)
sorted_data = sorted(data)

print("Mean:", mean)
print("Highest:", max_value)
print("Lowest:", min_value)
print("Sorted:", sorted_data)

The code above teaches iteration, built-in functions, and how to use Python as both a calculator and a simple data tool.

Students can then extend the activity to analyse real data — for example, daily temperatures, test results, or experiment readings — turning abstract numbers into trends and insights.

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Skills Highlight

• Creating and manipulating Python lists

• Using built-in functions like sum(), max(), min(), and sorted()

• Calculating statistical measures programmatically

• Applying coding to practical data handling in science and maths

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Why It Works in Teaching

Python bridges mathematics, computing, and science. Analysing data through code encourages logical thinking and problem solving. Students see instant feedback, gain confidence in coding, and learn a vital skill used in universities and industries worldwide.