Bioconductor for Genomic Data Science
This course is part of Genomic Data Science.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Genomic Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Install and use Bioconductor software effectively
Work with genomic data structures and sequences
Perform RNA-seq and microarray data analysis
Use annotation resources and databases
Implement advanced genomic data analysis workflows
Skills you'll gain
This course includes:
6.1 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course teaches the effective use of Bioconductor, a powerful open-source software project for the analysis of genomic data. Students learn to work with common data structures like ExpressionSets and GRanges, handle biological sequences, and perform analyses of various types of genomic data. The curriculum covers essential tools for modern genomic research, including RNA-seq analysis, microarray processing, and genomic annotation.
Module 1: Bioconductor Installation and Data Structures
Module 1 · 3 Hours to complete
Module 2: Biological Sequences and Genome Analysis
Module 2 · 1 Hours to complete
Module 3: Basic Data Types and Annotations
Module 3 · 1 Hours to complete
Module 4: Advanced Genomic Data Analysis
Module 4 · 2 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Genomic Data Science
Instructor
Biostatistics and Genomic Data Analysis Pioneer at Johns Hopkins
Dr. Kasper Daniel Hansen serves as an Assistant Professor at both the Johns Hopkins Bloomberg School of Public Health and the Johns Hopkins School of Medicine. With a Ph.D. in Biostatistics and Computational and Genomic Biology from the University of California, Berkeley, he has established himself as a leading expert in developing innovative methods for analyzing high-throughput biological data. His significant contributions to the analysis and interpretation of epigenetic data have advanced the field of genomic research. As one of the longest-serving contributors to the Bioconductor project and a member of its technical advisory board, Dr. Hansen continues to shape the landscape of computational biology and biostatistical analysis.
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