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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课程信息
您将获得的技能
- Statistics
- Data Analysis
- R Programming
- Biostatistics
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约翰霍普金斯大学
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.
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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.
审阅
- 5 stars54.54%
- 4 stars26.95%
- 3 stars11.28%
- 2 stars2.50%
- 1 star4.70%
来自基因组数据科学所需的统计学的热门评论
Pretty good but a little superficial and outdated.
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.
theoretical parts need more explanation. But in general, It is a well-structured course. thanks for your efforts
Great course as a starting point for statistical genomics!
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