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Statistics for Genomic Data Science

Course Summary

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.


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    Course Syllabus

    1. Experimental desgin

    • Types of data
    • Populations and samples
    • Bias and variance
    • Sample sizes 

    2. Exploratory analysis/data processing

    • Plots (scatter, box, MA, funnel, loess)
    • Plots to avoid
    • Tables

    3. Statistical inference

    • Normalization
    • Basic linear modeling
    • Batch effects
    • Multiple testing
    • Gene set analysis

    4. Common mistakes to avoid

    • Overfitting
    • Normalization errors
    • Confounders
    • Irreproducibility
    • Significance chasing

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    Recommended Background

    Prerequisite: Introduction to Sequencing Technologies
    Very strongly recommended: R programming is essentially a prerequisite for this course since Bioconductor builds on R programming. We don't require an R Programming certificate to enter this course, but you will need the skills and knowledge covered in R Programming to do well in this course. If you don't have those skills already, you should expect to struggle in this course.

    The target audience of this course are individuals in the molecular or computational sciences who want how to learn how to perform basic computational biology. This class is “hands on” and designed to get you started with the tools you need to perform Genomic Data Science.

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    Course Format

    No more than 2 hours of video lectures, a weekly quiz, and a course project.


Course Fee:
Free

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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