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Rafael Irizarry and Michael Love

Technological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.

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Technological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.

Using the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.

Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.

What you'll learn

  • Basic statistical concepts and R programming skills for analyzing data in the life sciences.
  • The underlying math of linear models useful for data analysis in the life sciences.
  • The techniques used to perform statistical inference on high-throughput and high-dimensional data.
  • Several techniques widely used in the analysis of high-dimensional data.

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What's inside

Four courses

Introduction to Linear Models and Matrix Algebra

(12 hours)
Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.

Statistical Inference and Modeling for High-throughput Experiments

(12 hours)
In this course, you'll learn various statistics topics including multiple testing problems, error rates, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data.

High-Dimensional Data Analysis

(12 hours)
If you’re interested in data analysis and interpretation, this data science course is for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principle component analysis.

Statistics and R

(12 hours)
This course teaches the R programming language in the context of statistical data and statistical analysis 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 code.

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