An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Statistics for Genomic Data Science
Statistics for Genomic Data Science
This course is part of Genomic Data Science Specialization
Instructor: Jeff Leek, PhD
Sponsored by BrightStar Care
37,197 already enrolled
Gain insight into a topic and learn the fundamentals.
4.2
(364 reviews)
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
92%
Most learners liked this course
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 4 modules in this course
This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
What's included
21 videos3 readings1 assignment
This week we will cover preprocessing, linear modeling, and batch effects.
What's included
14 videos1 assignment
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
What's included
15 videos1 assignment
In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.
What's included
14 videos1 reading1 assignment
Instructor
Instructor ratings
4.7 (35 ratings)Offered by
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
4.2
364 reviews
- 5 stars
54.67%
- 4 stars
26.37%
- 3 stars
11.81%
- 2 stars
2.19%
- 1 star
4.94%
Showing 3 of 364
TN
4
Reviewed on Jan 22, 2020
new material would be great as well as new datasets
CJ
5
Reviewed on Jul 15, 2019
It is really great that told me lots of basic statistical information that I didn't know.
TK
5
Reviewed on Mar 24, 2019
Very good course and useful understanding statistical aspects of data.
Recommended if you're interested in Data Science
Stanford University
Johns Hopkins University
Johns Hopkins University
University of Colorado Boulder
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy