Statistics for Data Science with Python

IBM connects Python coding with statistical summaries, probability, hypothesis tests and regression. The sequence ends with a Boston housing-data case and an exam. It offers a programming-based alternative to spreadsheet courses, with interpretation of test results treated as an outcome alongside the code used to calculate them.

What you will learn

• Calculate and visualize descriptive statistics in Python.
• Conduct and interpret hypothesis tests.
• Use regression within a documented data-analysis project.

Who this suits

Coursera labels the course mixed level. Detailed prerequisites are not specified in the published facts; preview the introductory Python material.

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.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Analyze a permitted sample dataset with two statistical tests. Explain the question each test answers and distinguish association from a causal conclusion.

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.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.