Statistics and Clustering in Python

The University of London links mathematical summaries with a Python clustering workflow. Learners move from means and deviations to two-dimensional data, then use pandas and k-means before a final project. This narrower scope helps separate an introduction to unsupervised grouping from a general survey of every statistical method.

What you will learn

• Calculate means and deviations using Python.
• Explore observations with two numerical dimensions.
• Use pandas and k-means in a clustering project.

Who this suits

Beginner-level learners; detailed prerequisites are not specified in the published course facts. Preview the expected Python exercises.

Before you enroll

Choose an exercise that makes the assumptions visible. A calculated result needs interpretation in the context of the sample, measurement method and question being investigated.

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Put it into practice

Cluster a small synthetic dataset and plot the result. Change the scale of one variable and explain why the groups may change.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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