This collection presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects.
%0 Book
%1 MenziesWilliamsZimmermann2016
%C Amsterdam
%D 2016
%E Menzies, Tim
%E Williams, Laurie
%E Zimmermann, Thomas
%I Morgan Kaufmann
%K 01624 103 book elsevier safari ai software development engineering process data pattern recognition analysis learn
%R http://www.sciencedirect.com/science/book/9780128042069
%T Perspectives on Data Science for Software Engineering
%U https://www.safaribooksonline.com/library/view/perspectives-on-data/9780128042618/
%X This collection presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects.
%@ 978-0-12-804206-9
@book{MenziesWilliamsZimmermann2016,
abstract = {This collection presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects.},
added-at = {2017-07-08T13:43:23.000+0200},
address = {Amsterdam},
biburl = {https://www.bibsonomy.org/bibtex/251f11b464f7c7b18ff38c52a6efb009e/flint63},
doi = {http://www.sciencedirect.com/science/book/9780128042069},
editor = {Menzies, Tim and Williams, Laurie and Zimmermann, Thomas},
file = {eBook:2016/MenziesWilliamsZimmermann2016.pdf:PDF;Amazon Search inside:http\://www.amazon.de/gp/reader/0128042060/:URL;Elsevier Product Page:https\://www.elsevier.com/books/perspectives-on-data-science-for-software-engineering/menzies/978-0-12-804206-9:URL},
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isbn = {978-0-12-804206-9},
keywords = {01624 103 book elsevier safari ai software development engineering process data pattern recognition analysis learn},
publisher = {Morgan Kaufmann},
timestamp = {2017-07-13T17:12:17.000+0200},
title = {Perspectives on Data Science for Software Engineering},
url = {https://www.safaribooksonline.com/library/view/perspectives-on-data/9780128042618/},
username = {flint63},
year = 2016
}