Healthcare
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Introduction to Statistics & Data Analysis in Public Health

Course Cover

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(8)

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Course Features

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Duration

16 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Beginner

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Teaching Type

Self Paced

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Video Content

16 hours

Course Description

Welcome to Statistics & Data Analysis for Public Health!

This course will help you understand the fundamental building blocks of statistical analysis, such as common distributions, types of variables and hypothesis testing. But, it will also teach you how to describe a data set that you have never seen before, its key features, strengths and quirks, run basic analyses, and formulate and test hypotheses based upon means and proportions. The course will give you the foundation to be able to do more advanced analysis or take the next courses in the series. The popular and flexible software R will be taught, which is widely used in statistics and machine-learning. This course is hands-on. You'll learn how to formulate a testable hypothesis using examples from medical research reported by the media. Next, you will work with a data set about vegetable and fruit eating habits. These data are realistically messy because that is what public health data sets look like in real life. To check your understanding, there will be mini-quizzes and feedback. This course will help you think critically and not just take things as they are. In this age of fake news and uncontrolled algorithms, these skills are even more essential. Prerequisites Although some formulae will be used to assist understanding, this course does not require a mathematics degree. Basic numeracy is not required (we won't use calculus), and you will need to be familiar with the graphical and tabular methods of presenting results. It is not necessary to have knowledge of R programming.

Course Overview

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Skills You Will Gain

What You Will Learn

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

Course Instructors

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Alex Bottle

Professor Medical Statistics

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 health...

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