Johns Hopkins University

Introduction to Genomic Technologies

Johns Hopkins University

Introduction to Genomic Technologies

This course is part of Genomic Data Science Specialization

Steven Salzberg, PhD
Jeff Leek, PhD

Instructors: Steven Salzberg, PhD

Access provided by SGCSRC

126,041 already enrolled

Gain insight into a topic and learn the fundamentals.

4,856 reviews

6 hours to complete
Flexible schedule
Learn at your own pace
96%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.

4,856 reviews

6 hours to complete
Flexible schedule
Learn at your own pace
96%
Most learners liked this course

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business
logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Genomic Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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".

What's included

8 videos2 readings1 assignment

In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.

What's included

3 videos1 assignment

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.

What's included

6 videos1 assignment

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.

What's included

10 videos2 readings2 assignments

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Instructor ratings
(832 ratings)
Steven Salzberg, PhD
Johns Hopkins University
2 Courses 156,894 learners

Offered by

Johns Hopkins University

Why people choose Coursera for their career

Felipe M.

Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

  • 5 stars

    66.37%

  • 4 stars

    26.95%

  • 3 stars

    5.08%

  • 2 stars

    0.94%

  • 1 star

    0.63%

Showing 3 of 4856

KL

Reviewed on Aug 1, 2021

CS

Reviewed on Jun 23, 2019

LK

Reviewed on Nov 16, 2019

View more reviews

Explore more from Data Science