Michael Love

 

 


 

Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard School of Public Health. Dr. Love received his bachelor’s in mathematics in 2001 from Stanford University, his master’s in statistics in 2010 from Stanford University, and his Ph.D. in Computational Biology in 2013 from the Department of Mathematics and Computer Science of the Freie Universität Berlin. His research focuses on inferring biologically meaningful patterns from high-throughput sequencing read counts. Dr. Love develops open-source statistical software for the analysis of exome sequencing and RNA sequencing experiments for the Bioconductor Project.




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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.

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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.

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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.

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Apr 15th 2016

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

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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