With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome. This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy. This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
Genomic Data Science Specialization
Be a next generation sequencing data scientist.. Master the tools and techniques at the forefront of the sequencing data revolution.
Offered By
What you will learn
Next generation sequencing experiments
Genomic technologies
DNA, RNA and epigenetic patterns
Genome analysis
Skills you will gain
How the Specialization Works
Take Courses
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Hands-on Project
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
There are 8 Courses in this Specialization
Introduction to Genomic Technologies
This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. This is the first course in the Genomic Data Science Specialization.
Genomic Data Science with Galaxy
Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.
Python for Genomic Data Science
This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Algorithms for DNA Sequencing
We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.
Instructors
James Taylor, PhD
Associate Professor of Biology and Computer Science
Jacob Pritt
Liliana Florea, PhD
Assistant Professor
Kasper Daniel Hansen, PhD
Assistant Professor, Biostatistics and Genetic Medicine
Mihaela Pertea, PhD
Assistant Professor
Steven Salzberg, PhD
Professor
Jeff Leek, PhD
Associate Professor, Biostatistics
Ben Langmead, PhD
Assistant ProfessorIndustry Partners
About Johns Hopkins University
Frequently Asked Questions
What is the refund policy?
Can I just enroll in a single course?
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Is financial aid available?
Can I take the course for free?
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
How long does it take to complete the Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-9 months.
How often is each course in the Specialization offered?
Each course in the specialization is offered monthly.
What background knowledge is necessary?
Some background in statistics, computer science, and biology is helpful but not required.
Do I need to take the courses in a specific order?
The sequence shown above is strongly recommended.
Will I earn university credit for completing the Specialization?
No, you will not earn credit at Johns Hopkins University by completing this specialization.
What will I be able to do upon completing the Specialization?
Upon completion, you will be able to use a variety of tools to conduct analysis of data generated by next generation sequencing experiments.
When will I be able to enroll?
Pre-enrollment will be available on January 11, 2016.
More questions? Visit the Learner Help Center.