Genomics Data Analysis XSeries

The Genomics Data Analysis XSeries is an advanced series that will enable students to analyze and interpret data generated by modern genomics technology.
Using open source software, including R and Bioconductor, you will acquire skills to analyze and interpret genomic data.
This XSeries is perfect for those who seek advanced training in high-throughput technology data. Problem sets will require coding in the R language to ensure learners fully grasp and master key concepts. The final course investigates data analysis for several experimental protocols in genomics.

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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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