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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Introduction to Linear Models and Matrix Algebra (edX) EdX
HarvardX,Harvard University

Introduction to Linear Models and Matrix Algebra (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Discover the essential principles of Linear Models and Matrix Algebra in this introductory data analysis course on edX. Perfect for those interested in life sciences, this program teaches you how to represent complex analyses with matrix algebra and perform statistical inference using R programming. Enhance your understanding of experimental design and high-dimensional data analysis.

Self Paced
Self-Paced
Algebra Linear Algebra Matrix
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Introduction to Bioconductor (edX) EdX
HarvardX,Harvard University

Introduction to Bioconductor (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive into the world of genomics with 'Introduction to Bioconductor' on edX. This course unravels the complexities of genome-scale assays, offering insights into next-generation sequencing, microarrays, and how to effectively analyze and interpret genomic data using R and Bioconductor tools. Whether you're a biologist, bioinformatician, or just curious about genomics, this course provides a solid foundation.

Self Paced
Self-Paced
Genome Statistical Methods Genomics
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Advanced Bioconductor (edX) EdX
HarvardX,Harvard University

Advanced Bioconductor (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive deep into the world of genomics with our Advanced Bioconductor course. Gain expertise in visualizing genome-scale data, building interactive interfaces for discovery, and mastering reproducible analysis through knitr and rmarkdown. Explore data architecture and analyze large consortium-generated datasets at scale.

Self Paced
Self-Paced
Parallel Computing Biological Data Biocondutor
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Case Studies in Functional Genomics (edX) EdX
HarvardX,Harvard University

Case Studies in Functional Genomics (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive into the world of functional genomics with our expert-led course. Master advanced techniques such as RNA-Seq, ChIP-Seq, and DNA methylation data analysis using powerful open source software including R and Bioconductor. Gain insights into processing raw genomic data to answer complex biological questions.

Self Paced
Self-Paced
Data Analysis Biological Data Genomics
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Statistics and R (edX) EdX
HarvardX,Harvard University

Statistics and R (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive into the world of data analysis with 'Statistics and R' on edX. This course is perfect for beginners in the life sciences who want to understand basic statistical concepts and learn how to use R programming to analyze data. Gain proficiency in computing p-values and constructing confidence intervals, all while enhancing your R skills.

Self Paced
Self-Paced
Statistics Data Analysis Statistical Inference
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Statistical Inference and Modeling for High-throughput Experiments (edX) EdX
HarvardX,Harvard University

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

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive into Statistical Inference and Modeling for High-throughput Experiments to gain a deep understanding of statistical techniques applied to large-scale biological datasets. This course covers essential topics such as error rate controlling procedures, false discovery rates, q-values, and parametric modeling with applications in high-throughput data analysis.

Self Paced
Self-Paced
Statistics Modeling Statistical Inference
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