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Data science as a science Leek, Jeff
Description
We all know that any genomic data analysis involves hundreds of decisions by any analyst. We have good theoretical methods for controlling error rates and preventing false discoveries for single methods. But what happens when humans get their hands on our methods and code? In this talk I propose a new framework for modeling data analysis and show some early experimental results in our effort to make data science a rigorous empirical science.
Item Metadata
| Title |
Data science as a science
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| Creator | |
| Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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| Date Issued |
2017-03-30T09:26
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| Description |
We all know that any genomic data analysis involves hundreds of decisions by any analyst. We have good theoretical methods for controlling error rates and preventing false discoveries for single methods. But what happens when humans get their hands on our methods and code? In this talk I propose a new framework for modeling data analysis and show some early experimental results in our effort to make data science a rigorous empirical science.
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| Extent |
21 minutes
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| Subject | |
| Type | |
| File Format |
video/mp4
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| Language |
eng
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| Notes |
Author affiliation: Johns Hopkins Bloomberg School of Public Health
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| Series | |
| Date Available |
2017-09-27
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0355796
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| URI | |
| Affiliation | |
| Peer Review Status |
Unreviewed
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| Scholarly Level |
Faculty
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International