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Johns Hopkins University

Statistics for Genomic Data Science

Johns Hopkins University via Coursera


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.


  • 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


1.7 rating, based on 3 Class Central reviews

4.2 rating at Coursera based on 353 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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