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.
This course is part of the Genomic Data Science Specialization
Introduction to Genomic Technologies
Offered By
About this Course
34,814 recent views
Skills you will gain
BioinformaticsStatisticsData ScienceComputational Biology
Syllabus - What you will learn from this course
Week
12 hours to complete
Overview
In this Module, you can expect to study topics of "Just enough molecular biology", "The genome", "Writing a DNA sequence", "Central dogma", "Transcription", "Translation", and "DNA structure and modifications".
8 videos (Total 81 min), 2 readings, 1 quiz
8 videos
Just Enough Cell Biology8m
Important Molecules in Molecular Biology7m
The Human Genome Project17m
Molecular Biology Structures9m
From Genes to Phenotypes9m
2 readings
Syllabus10m
Pre-Course Survey10m
1 practice exercise
Quiz 1: Overview and Molecular Biology20m
Week
21 hour to complete
Measurement Technology
In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
3 videos (Total 25 min), 1 quiz
1 practice exercise
Quiz 2: Measurement Technology20m
Week
31 hour to complete
Computing Technology
The lectures for this module cover a few basic topics in computing technology. We'll go over the foundations of computer science, algorithms, memory and data structures, efficiency, software engineering, and computational biology software.
6 videos (Total 39 min), 1 quiz
6 videos
Efficiency3m
Software Engineering8m
What is Computational Biology Software11m
1 practice exercise
Quiz 3: Computing Technology20m
Week
42 hours to complete
Data Science Technology
In this module on Data Science Technology, we'll be covering quite a lot of information about how to handle the data produced during the sequencing process. We'll cover reproducibility, analysis, statistics, question types, the central dogma of inference, analysis code, testing, prediction, variation, experimental design, confounding, power, sample size, correlation, causation, and degrees of freedom.
10 videos (Total 55 min), 2 readings, 2 quizzes
10 videos
Data Sharing Plans4m
Getting Help with Statistics2m
Plotting Your Data5m
Sample Size and Variability7m
Statistical Significance6m
Multiple Testing7m
Study Design, Batch Effects, and Confounding7m
2 readings
Course Project Instructions and Reading10m
Post-Course Survey10m
2 practice exercises
Quiz 4: Data Science Technology20m
Course Project20m
4.5
274 Reviews38%
started a new career after completing these courses
29%
got a tangible career benefit from this course
Top reviews from Introduction to Genomic Technologies
Relatively nice introduction course, contents are maybe rather limited, yet as an instructive course, it does provide a clear overview which correlates well with the required answers for the quizzes!
Very good introductory course. It's a lot of material to cover for a newbie, but if you have some background, you will find you progress pretty quickly. Enjoyable, engaging, fun and useful.
Instructors
Steven Salzberg, PhD
ProfessorBiomedical Engineering, Computer Science, and Biostatistics
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
About Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
About the Genomic Data Science Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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