Introduction to Statistics & Data Analysis in Public Health

Imperial introduces statistical thinking through public-health examples and an initial R workflow. Variables, distributions and sampling prepare learners for hypothesis testing. This is a starting point for reading and producing quantitative analysis before taking the series’ more advanced regression courses.

What you will learn

• Distinguish types of health-data variables.
• Describe sampling and common distributions.
• Begin a hypothesis-testing workflow in R.

Who this suits

Beginners with an interest in quantitative data and familiarity with standard graphs and tables, as stated by the provider.

Before you enroll

This is education about populations and evidence, rather than individual treatment advice. Check the dates and setting of any statistics or policy examples. A completion certificate does not replace a professional public-health or clinical qualification.

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

Use a small fictional health dataset to summarize a variable, plot its distribution and describe the question a statistical test would address.

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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