Rafael Irizarry

Dr. Irizarry received his bachelor’s in mathematics in 1993 from the University of Puerto Rico and his Ph.D. in statistics in 1998 from the University of California, Berkeley. He joined the faculty of the Department of Biostatistics in the Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute and a Professor of Biostatistics at Harvard School of Public Health. Dr. Irizarry has worked on the analysis and pre-processing of microarray, next-generation sequencing, and genomic data, and is currently interested translational work, developing diagnostic tools and discovering biomarkers. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data.
More info here.

Sort options

Data Science: Capstone (edX)

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the [...]

Data Science: Machine Learning (edX)

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques. Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using [...]

Data Science: Linear Regression (edX)

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science. Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding.

Data Science: Inference and Modeling (edX)

Learn inference and modeling, two of the most widely used statistical tools in data analysis. Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election [...]

Data Science: Wrangling (edX)

Learn to process and convert raw data into formats needed for analysis. In this course, we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling [...]

Data Science: Productivity Tools (edX)

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio. A typical data analysis project may involve several parts, each including several data files and different scripts with code. Keeping all this organized can be challenging.

Data Science: Visualization (edX)

Learn basic data visualization principles and how to apply them using ggplot2. This course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R.

Data Science: Probability (edX)

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008. In this course, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008. Part of what caused [...]

Data Science: R Basics (edX)

Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the [...]

High-Dimensional Data Analysis (edX)

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 [...]