Introduction to Genomic Technologies
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
Understand the fundamentals of molecular biology and genomics
Learn about next-generation sequencing technologies and applications
Grasp basic concepts in computing and data structures
Master key statistical concepts for genomic data analysis
Develop skills in handling and analyzing sequencing data
Skills you'll gain
This course includes:
3.3 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course introduces students to the basic biology of modern genomics and the experimental tools used to measure it. The curriculum covers the Central Dogma of Molecular Biology and explains how next-generation sequencing can measure DNA, RNA, and epigenetic patterns. Students also learn key concepts in computing and data science necessary for analyzing next-generation sequencing experimental data. The course combines theoretical knowledge with practical applications in genomic data analysis.
Overview
Module 1 · 2 Hours to complete
Measurement Technology
Module 2 · 54 Minutes to complete
Computing Technology
Module 3 · 1 Hours to complete
Data Science Technology
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
Instructors
Chief Data Officer and J Orin Edson Foundation Chair at Fred Hutchinson Cancer Center
Dr. Jeff Leek serves as the Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. He earned his PhD in Biostatistics from the University of Washington and is known for his significant contributions to genomic data analysis and statistical methods for personalized medicine. His research has advanced our understanding of molecular mechanisms related to brain development, stem cell self-renewal, and immune responses to trauma, with findings published in top scientific journals such as Nature and Proceedings of the National Academy of Sciences. Dr. Leek developed a highly acclaimed Data Analysis course for Biostatistics students at Johns Hopkins, which has consistently received teaching excellence awards. He is also recognized for his efforts in creating educational initiatives that leverage data science for public health and economic development, including massive open online courses that have engaged millions worldwide.
Distinguished Computational Biology and Genomics Expert at Johns Hopkins
Dr. Steven Salzberg serves as Professor of Biomedical Engineering, Computer Science, and Biostatistics at Johns Hopkins University, where he also directs the Center for Computational Biology and is a member of the McKusick-Nathans Institute of Genetic Medicine. His research group specializes in developing cutting-edge computational methods for DNA analysis using the latest sequencing technologies, making significant contributions to gene finding, genome assembly, comparative genomics, and evolutionary genomics. Beyond his groundbreaking research in DNA and RNA sequencing with next-generation technology, Dr. Salzberg is also known for his public engagement through his Forbes science blog, where he addresses critical issues ranging from pseudoscience and alternative medicine to gene patents and higher education, making complex scientific concepts accessible to the public.
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