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Johns Hopkins University

Statistics for Genomic Data Science

Johns Hopkins University via Coursera

Overview

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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.

Syllabus

  • Module 1
    • 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.
  • Module 2
    • This week we will cover preprocessing, linear modeling, and batch effects.
  • Module 3
    • This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
  • Module 4
    • 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.

Taught by

Jeff Leek

Reviews

1.7 rating, based on 3 Class Central reviews

4.2 rating at Coursera based on 351 ratings

Start your review of Statistics for Genomic Data Science

  • Brandt Pence
    This is the final course in the Genomic Data Science specialization from Johns Hopkins. This course covers some statistical techniques in genomics using R and Bioconductor packages. It has most of the same problems as the previous courses in this…
  • Anonymous
    The course has the same problems as most of the courses in the Specialisation. It does not give you the tools to to the excersies. Furthermore, it feels like a review of methods which require a good deal of background knowledge to unterstand. The R…

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