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Statistics for Genomic Data Science

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Course Features

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Duration

9 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

9 hours

Course Description

This course introduces the statistics that underlie the most successful genomic data science projects. This course is part of the Genomic Big Data Science Specialization at Johns Hopkins University.

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Skills You Will Gain

What You Will Learn

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

Preprocessing, linear modeling, and batch effects

Modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing

General pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies

Course Instructors

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Jeff Leek, PhD

Associate Professor, Biostatistics

Jeff Leek is an Assistant Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and co-editor of the Simply Statistics Blog. He received his Ph.D. in Biostatistics ...

Course Reviews

Average Rating Based on 8 reviews

4.9

88%

13%

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