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.




Customize your search:

E.g., 2017-08-18
E.g., 2017-08-18
E.g., 2017-08-18
Self Paced

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 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.

Average: 4.7 (3 votes)
Self-Paced

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 way that will help make the connection between concepts and implementation.

No votes yet
Self-Paced

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 is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.

No votes yet

Self-Paced

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 the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis.

No votes yet
Apr 15th 2016

Explore data analysis of several experimental protocols, using open source software, including R and Bioconductor.

No votes yet
Mar 15th 2016

Learn how to bridge from diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing.

Average: 9.5 (2 votes)

Feb 15th 2016

The structure, annotation, normalization, and interpretation of genome scale assays.

Average: 6 (3 votes)
Jan 15th 2016

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Average: 8.3 (4 votes)
Dec 15th 2015

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Average: 6.5 (2 votes)
Oct 15th 2015

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

No votes yet