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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À propos de ce cours
Compétences que vous acquerrez
- Statistics
- Data Analysis
- R Programming
- Biostatistics
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Université Johns-Hopkins
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.
Programme de cours : ce que vous apprendrez dans ce cours
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.
Avis
- 5 stars54,54 %
- 4 stars26,95 %
- 3 stars11,28 %
- 2 stars2,50 %
- 1 star4,70 %
Meilleurs avis pour STATISTICS FOR GENOMIC DATA SCIENCE
theoretical parts need more explanation. But in general, It is a well-structured course. thanks for your efforts
Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.
This is the best. It opens my eye for genomic data analysis.
Pretty good but a little superficial and outdated.
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