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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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.
강의 계획 - 이 강좌에서 배울 내용
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.
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STATISTICS FOR GENOMIC DATA SCIENCE의 최상위 리뷰
Overall, a very good course. Not without its flaws (inconsistent video audio levels), but I have walked away knowing far more about Genomic Data Science than I expected to.
The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!
sometimes termininology was used interchangeably, which can be confusing for a beginner but overall a good introduction to statistcs for genomic data analysis
Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.
Genomic Data Science 특화 과정 정보
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.

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