Statistical Analysis with R for Public Health Specialization

There are statistics all over the place. There's a good chance it'll rain today. Unemployment rate trends over time. India's chances of winning the next Cricket World Cup. Football, for example, began as a bit of fun but has since developed into a large business. In medicine, statistical analysis plays an important role, not least in the wide and basic field of public health.

This specialism will provide you an overview of medical research and how – and why – you can turn a hazy idea into a scientifically testable hypothesis. You'll learn about sampling, uncertainty, variation, missing values, and distributions, among other statistical concepts. Then, using R, one of the most widely used and versatile free software packages around, you'll get your hands dirty analyzing data sets covering some major public health issues, such as fruit and vegetable consumption and cancer, diabetes risk factors, and predictors of death following heart failure hospitalization.

This specialism consists of four courses – statistical reasoning, linear regression, logistic regression, and survival analysis – and will be offered as part of the future Global Master in Public Health program, which will begin in September 2019. The speciality can be pursued independently of the GMPH and requires no prior understanding of statistics or the R programming language. All you need is an interest in medical issues as well as quantitative data.

Each course will present essential ideas as well as a data collection that will be used as a working example throughout the course. Missing numbers and strange distributions are all too typical in public health data. Real or simulated data from real patient-level data sets will be used.

As you encounter typical data and analytical issues to solve and debate with your other learners, the emphasis will be on "learning through doing" and "learning through discovering." Before accessing the answers and explanations offered by the teachers, you'll have the opportunity to sort things out for yourself and with your peers.

What you will learn

• Recognize the essential elements of statistical reasoning in order to justify statistics' crucial role in modern public health research and practice.
• As a first step toward more advanced analysis with R software, describe a given data set from scratch using descriptive statistics and graphical methods.
• In R, use appropriate methods to formulate and investigate statistical relationships between variables inside a data collection.
• Interpret the outcomes of your analysis and consider the role of chance and bias in explaining your findings.

Skills you will gain

• Statistical Thinking
• Survival Analysis
• Logistic Regression
• Data analysis with R
• Linear Regression
• Run basic analyses in R
• R Programming
• Understand common data distributions and types of variables
• Formulate a scientific hypothesis
• Correlation And Dependence

Instructors: Alex Bottle, Victoria Cornelius

Coursera rate: 4.7/5.0, 1.341 ratings

Offered by: Imperial College London

Enroll here: https://www.coursera.org/specializations/statistical-analysis-r-public-health