Statistics for Genomic Data Science

Por: Coursera . en: , ,

  • 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.

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