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
This course is part of Genomic Data Science Specialization
Instructor: Jeff Leek, PhD
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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
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Recommended if you're interested in Probability and Statistics
University of California San Diego
University of Illinois Urbana-Champaign
Johns Hopkins University
University of Colorado System
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Reviewed on Jun 27, 2018
The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!
Reviewed on Jun 30, 2020
theoretical parts need more explanation. But in general, It is a well-structured course. thanks for your efforts
Reviewed on Jul 15, 2019
It is really great that told me lots of basic statistical information that I didn't know.
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