July 2016
Beginner to intermediate
408 pages
10h 58m
English
book
Perspectives on Data Science for Software Engineering
by Tim Menzies, Laurie Williams, Thomas Zimmermann
Content preview from Perspectives on Data Science for Software Engineering
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The white-shirt effect
Learning from failed expectations
L. Prechelt Freie Universität Berlin, Berlin, Germany
Abstract
No matter how enthusiastic you are about your youngest data-scientific idea: Before you invest a lot of effort into data collection and/or analysis, consider the possibility that the data may show something other than you expect – or even nothing at all. Always hypothesize a causational model of what is going on, define fallback positions in case that model happens to be wrong, and make your investments in small steps, validating your expectations after each of them. This chapter explains those rules by a negative example in an entertaining way.
Keywords
DCDIM; Causation mechanism; Causation model; Partial checks; Face ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.