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.
Offered By
About this Course
Skills you will gain
- Statistics
- Data Analysis
- R Programming
- Biostatistics
Offered by

Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
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.
Reviews
- 5 stars54.54%
- 4 stars26.95%
- 3 stars11.28%
- 2 stars2.50%
- 1 star4.70%
TOP REVIEWS FROM STATISTICS FOR GENOMIC DATA SCIENCE
Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.
Great course as a starting point for statistical genomics!
It is really great that told me lots of basic statistical information that I didn't know.
Very helpful and i understood i should master statistics and do more research
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