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Imperial College London

Introduction to Statistics & Data Analysis in Public Health

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. Prerequisites 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.

Status: Public Health
Status: R Programming
BeginnerCourse16 hours

Featured reviews

SS

5.0Reviewed Sep 15, 2021

a​ methodical way to understand statistics although focus is public health. the lecture open your prospctive to other industries in subtle ways, I also recommend ICL courses.

PD

5.0Reviewed Sep 1, 2020

An enjoyable course in Stats for complete beginners. It is a good mix of videos, exercises and quizes that kept my interest up. I shall go on to the next module in this course.

CM

5.0Reviewed Nov 2, 2021

T​his course is an excellent introduction to statistical analysis in public health. In addition, the course provides a useful start to the application of R in statistical analysis.

LA

5.0Reviewed May 25, 2019

Was a very nicely done and clear course to build or re-build foundation for most common statistical concepts and an intro to using R via R-Studio for your work with them on the basics.

HP

5.0Reviewed Sep 5, 2020

This course is great. I like how it is structured and the feedback after activities. Combines theory and practices in an efficient way. Congratulations.

CW

5.0Reviewed May 27, 2020

Fantastic. Just what I needed as a doctor wanting to learn more about statistics. R is an amazing alternative to other programs such as STATA and SPSS.

NY

5.0Reviewed Apr 18, 2020

Great introduction towards statistics in public health, with nice addition of R programming language. The material is meticulously explained and easy to understand.

RE

5.0Reviewed Aug 13, 2020

Excellent course with really good teaching. Felt like it developed a good grounding in the basic topics. Would recommend having a little R experience prior to taking this course.

BR

4.0Reviewed Jun 29, 2020

Challenging but very informative. Sometimes I was not clear on the concept but usually there was a discussion a couple of clicks away which elucidated things,

DK

4.0Reviewed May 13, 2021

This is a great course for Introduction to Statistics & Data Analysis in Public Health. It covers all the necessary topics and provides a well designed learning structure.

MC

5.0Reviewed Dec 9, 2025

Excellent introductory course to statistics in public health with exposure to RStudio. Highly recommend to any clinician interested in public health studies who has an interest in data analysis.

XF

4.0Reviewed Aug 16, 2019

Great course to start learning statistics in public health. I love the example data that are closely related to public health field. Thank you very much!

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