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 an open source free 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.
A focus on several techniques that are widely used in the analysis of high-dimensional data. If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of [...]
A focus on the techniques commonly used to perform statistical inference on high throughput data. In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it [...]
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a [...]
Learn to use R programming to apply linear models to analyze data in life sciences. Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that [...]