Introduction to Statistics & Data Analysis in Public Health

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health.In this specialisation, you'll take a peek at what medical research is and how " and indeed why " you turn a vague notion into a scientifically testable hypothesis. You'll learn about key statistical concepts like sampling, uncertaint

( 46 Reviews )

Created by: Alex Bottle

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Overall Score : 96 / 100

Course Description

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.PrerequisitesSome 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.

Time: 12h

Instructor Details

Alex Bottle

4.8 Rating
46 Reviews

Prof. Alex Bottle is Professor in Medical Statistics and co-director of the Dr Foster Unit at Imperial. His research focuses on measuring and explaining variations in the quality and safety of healthcare using large databases. He teaches on a range of undergraduate and postgraduate degree courses and supervises and examines PhDs; his current students work in diverse areas such as surgery, cardiovascular risk and digital health. He also trains the UK public sector in statistical thinking.

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By Ã¦ÂŸÂ´Ã©Â•Â¿Ã¥ÂÂ¤ on 21-Aug-19

AprendÃƒÂ mucho con una metodologÃƒÂa bastante didÃƒÂ¡ctica.

By Vishakh U on 22-Sep-19

Could be way better. AP level statistics should be recommended before starting this course.

By Michelle W on 13-Oct-19

The instructor is clear and engaging. I enjoyed the real world examples that were used. My only reason for less than 5 stars is the amount of reading in week 3/4. I very much prefer videos to introduce the concepts, and then perhaps additional readings for further information... More video please!

By Xi F on 17-Aug-19

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!

By Shaswat L on 3-Aug-19

This course covers up the very basic and important aspects of Statistics and Hypothesis Testing. What could have still been added were some more complex scenarios where this knowledge can be used and Hypothesis could be formulated.

By Jiracha J on 3-Aug-19