Introduction to Statistics and Data Analysis in Public Health Coursera

Overview

This Introduction to Statistics and Data Analysis in Public Health course offered by Coursera in partnership with Imperial College London 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.

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