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Publicado por Morgan Kaufmann 2016-07-01, 2016
ISBN 10: 0128042060 ISBN 13: 9780128042069
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Idioma: Inglés
Publicado por Elsevier Science & Technology, 2016
ISBN 10: 0128042060 ISBN 13: 9780128042069
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Idioma: Inglés
Publicado por Morgan Kaufmann 2016-07-01, 2016
ISBN 10: 0128042060 ISBN 13: 9780128042069
Librería: Chiron Media, Wallingford, Reino Unido
EUR 73,25
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Añadir al carritoTaschenbuch. Condición: Neu. Perspectives on Data Science for Software Engineering | Tim Menzies (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2016 | Elsevier Inc | EAN 9780128042069 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Añadir al carritoCondición: New. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security a.
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware - Perspectives on Data Science for Software Engineering 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. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. 410 pp. Englisch.
Idioma: Inglés
Publicado por Elsevier Science & Technology, 2016
ISBN 10: 0128042060 ISBN 13: 9780128042069
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 89,56
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Añadir al carritoPaperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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EUR 59,73
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Perspectives on Data Science for Software Engineering 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. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.