Specialization Statistical Analysis with R for Public Health

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 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.

Applied Learning Project:

Each course Specialization Statistical Analysis with R 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 (all anonymised and with usage permissions in place).

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.

LEARN STEP BY STEP:

• 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

Rating: 4.7/5

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