Online Course
Statistics for Genomic Data Science
- Provider Coursera
- Cost Free Online Course (Audit)
- Session In progress
- Language English
- Certificate Paid Certificate Available
- Effort 5-7 hours a week
- Duration 4 weeks long
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Overview
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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.
Syllabus
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.
-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
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Reviews for Coursera's Statistics for Genomic Data Science Based on 3 reviews
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- 4 star 0%
- 3 star 33%
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- 1 stars 67%
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Brandt P
by
Brandt
completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
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 specialization in that the work is at a level for which the student really needs some significant background in the technical aspects in order to complete the course. Fortunately, I have enough background in statistics and R programming that I was able to complete this course fairly easily, but this will not be the case for most people without this…
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Anonymous
Anonymous
completed this course.
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 part is ok I guess but as somebody already mentioned, you will not get arround guessing about a third of the anwesers and even more of the times you will have to guesstimate by getting results which are close to the real anwesers. This is because stuff changes in R and for those of us who work with R and have to keep their stuff up to date it is hard to rollback. I would not recommend nor would I do this course again. If made the mistake of buying the Specialisation at the beginning. I consider it wasted money.
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