What you'll learn
- Basic statistical concepts and R programming skills necessary for analyzing data in the life sciences
- The underlying mathematical basics of linear models useful for data analysis in the life sciences
- The techniques commonly used to perform statistical inference on high throughput data
- Several techniques that are widely used in the analysis of high-dimensional data
About this series
Currently, biomedical research groups around the world are producing more data than they can handle.
The training and skills acquired by taking the Data Analysis for Life Sciences XSeries will result in greater success in biological discovery and improving individual and population health.
In this XSeries, you will gain the tools to analyze and interpret life sciences data. You will learn the basic statistical concepts and R programming skills necessary for analyzing real data.
R is a free open-source statistical software and is the most widely used data analysis platforms among academic statisticians.
Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, who for the past 15 years has focused on the analysis of genomics data, this XSeries is perfect for anyone in the life sciences who wants to learn how to analyze data. Problem sets will require coding in the R language to ensure learners fully grasp and master key concepts.
This series includes
- A focus on the techniques commonly used to perform statistical inference on high throughput data.
Harvard T.H. Chan School of Public Health