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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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 [...]
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Statistical Inference and Modeling for High-throughput Experiments (edX)

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 [...]
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Statistics and R (edX)

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 [...]
10
Average: 10 ( 3 votes )

Introduction to Linear Models and Matrix Algebra (edX)

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 [...]
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Average: 3 ( 2 votes )

Case Studies in Functional Genomics (edX)

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor. We will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions. [...]
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Advanced Bioconductor (edX)

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data. In this course, we begin with approaches to visualization of genome-scale data, and provide tools to build interactive graphical interfaces to speed discovery and interpretation. Using knitr and rmarkdown as basic authoring [...]
10
Average: 10 ( 2 votes )

Introduction to Bioconductor (edX)

The structure, annotation, normalization, and interpretation of genome scale assays. We begin with an introduction to the biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental [...]
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Average: 1 ( 2 votes )

Case study: DNA methylation data analysis (edX)

Basic workflow for analyzing DNA methylation data. The course is running Self-Paced through September 15th, 2015.
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PH525x: Data Analysis for Genomics (edX)

Data Analysis for Genomics will teach students how to harness the wealth of genomics data arising from new technologies, such as microarrays and next generation sequencing, in order to answer biological questions, both for basic cell biology and clinical applications.
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