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
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Statistics for Genomic Data Science
This course is part of Genomic Data Science Specialization
Instructor: Jeff Leek, PhD
36,900 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.2
(363 reviews)
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
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Assessments
4 assignments
Taught in English
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This course is part of the Genomic Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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
Recommended if you're interested in Probability and Statistics
Fred Hutchinson Cancer Center
Johns Hopkins University
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