Introduction to Statistics & Data Analysis in Public Health


Alex Bottle

Welcome to Introduction to Statistics & Data Analysis in Public Health!

This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever.


Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.

Defend the critical role of statistics in modern public health research and practice

Describe a data set from scratch, including data item features and data quality issues, using descriptive statistics and graphical methods in R

Select and apply appropriate methods to formulate and examine statistical associations between variables within a data set in R

Interpret the output from your analysis and appraise the role of chance and bias

Read more.

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AlgorithmsApplied MathematicsConfidence IntervalsData AnalysisData SetsDistributionsHealthcare AnalyticsMachine LearningProgrammingStatistical AnalysisVariables

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