subject

Coursera: Statistics for Genomic Data Science

 with  Jeff Leek
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.

3 Student
reviews
Cost Free Online Course (Audit)
Subject Bioinformatics
Provider Coursera
Language English
Certificates Paid Certificate Available
Hours 5-7 hours a week
Calendar 4 weeks long
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3 reviews for Coursera's Statistics for Genomic Data Science

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1 out of 1 people found the following review useful
2 years ago
Brandt Pence 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 rea Read More
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 background.

The course covered exploratory analysis, clustering, regression, batch effects, generalized linear models, p-values, and several other topics, and the final week also included an introduction to some of the more common genomic experiments (RNA-seq, ChIP-seq, GWAS, etc.). Similar to the Bioconductor class, there were no peer-reviewed assignments here, only 1 quiz at the end of each week. The material covered in the lectures was for the most part sufficient to complete the programming needed in order to get the answers to these quizzes. One note is that because Bioconductor packages change so frequently, for some of the questions I was only able to get answers that were somewhat close to what ended up being the correct choice on the quiz, so students need to take care to either realize this or to download and use the versions of the packages used by the instructor when creating the course. This problem is likely to get worse over time.

Overall, three stars. A fair class for someone with an interest in this field who also happens to have a decent background in R programming.
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12 months ago
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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 Read More
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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0 out of 8 people found the following review useful
2 years ago
Colin Khein completed this course.
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