Coursera – Statistical Analysis with R for Public Health Specialization 2025-3

Apr 18, 2025 - 19:42

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Statistical Analysis with R for Public Health Specialization, 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, uncertainty, variation, missing values and distributions.

Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data.

What you’ll learn

Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice
Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software
Apply appropriate methods in order 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 as explanations for your results