Descriptive vs. Inferential Statistics: What’s the Difference?
November 7, 2024
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This course is part of Genomic Data Science Specialization
Instructor: Jeff Leek, PhD
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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.
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.
21 videos3 readings1 assignment
This week we will cover preprocessing, linear modeling, and batch effects.
14 videos1 assignment
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 videos1 assignment
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.
14 videos1 reading1 assignment
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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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371 reviews
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Reviewed on Feb 11, 2017
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.
Reviewed on Jul 15, 2019
It is really great that told me lots of basic statistical information that I didn't know.
Reviewed on Mar 24, 2019
Very good course and useful understanding statistical aspects of data.
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