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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このコースについて
習得するスキル
- Statistics
- Data Analysis
- R Programming
- Biostatistics
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ジョンズ・ホプキンズ大学(Johns Hopkins University)
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.
シラバス - 本コースの学習内容
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.
レビュー
- 5 stars54.20%
- 4 stars27.10%
- 3 stars11.21%
- 2 stars2.49%
- 1 star4.98%
STATISTICS FOR GENOMIC DATA SCIENCE からの人気レビュー
theoretical parts need more explanation. But in general, It is a well-structured course. thanks for your efforts
new material would be great as well as new datasets
Very good course and useful understanding statistical aspects of data.
It is really great that told me lots of basic statistical information that I didn't know.
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