Statistics for Genomic Data Science

9 hours to complete
Flexible Schedule

Jeff Leek, PhD

Skills You’ll Gain

Exploratory Data Analysis Statistical Inference Statistical Methods Data Pipelines Probability & Statistics Regression Analysis Statistical Analysis Biostatistics R Programming Bioinformatics Data Processing Statistical Hypothesis Testing Data Transformation Statistical Modeling

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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.

This week we will cover preprocessing, linear modeling, and batch effects.

This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.

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.