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Predictive Analytics, Data Mining and Big Data

Myths, Misconceptions and Methods

"Predictive analytics, big data, and data mining are key topics for organizations who want to leverage the ever increasing amounts of data that organizations hold about their customers and other individuals. This in-depth guide provides readers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, the pitfalls and dangers, and a contextual road map for developing solutions that deliver benefits to their organizations. Written in an accessible way, this 'how-to-guide' will help managers to make the most of these technologies in their business area"--
Rezension
'A welcome addition to the literature on data driven decision making. Finlay's book gives a commendably non-technical discussion of the business issues associated with embedding analytics into an organisation and how data, big and small, can be used to support better decision making. It is peppered with case studies from the author's experience and is a great source of insight for technicians and business people alike.'
-Paul Russell, Director of Analytics, Experian UK&I
'A fully immersive introduction to the world of predictive analytics and its application to Big (and small) Data. Full of interesting stories and case studies, it provides a fascinating real world perspective of these technologies and how best to apply them. A must read for managers and data scientists alike.'
-Ioannis Stamatopoulos, Director for Moody's Enterprise Risk Solutions and Services, RiskMatrix
'Analytics is the latest organizational enthusiasm harnessing data, both internal and external, to add value is no easy task. This introduction hits all the right notes with case studies and insight gathered from Steve Finlay's considerable experience. The challenge which he meets is to explain in clear non-technical language the various methods and how they can be implemented; nor does he neglect the problems of embedding quantitative expertise into organizations that aren't used to its logic. Recommended for the manager or MBA student wanting an overview of this exciting new area.'
-Professor Robert Fildes, Distinguished Professor, Director, Lancaster Centre for Forecasting, Lancaster University, UK
'Mr Finlay has written a very readable, business friendly book that goes well beyond the formula. His real world experience and practical discussions would be of great benefit to industry practitioners.'
-Naeem Siddiqi, Global Product Manager, Banking Analytics Solutions, SAS Institute
"If you want an excellent nontechnical overview of the predictive analytics process, this book should meet your needs... Finlay, a data scientist with decades of experience, provides an excellent introduction for readers, equipping them with the knowledge
to manage both the implementation and use of predictive analytics models in their organizations."
-Neil Desnoyers, Decision & System Sciences Department, Saint Joseph's University, Philadelphia, Pennsylvania: review in Interfaces, INFORMS
Portrait

Steven Finlay is one of the UK's leading experts on predictive analytics and its application within Big Data environments. He has extensive experience of developing predictive analytics solutions within Financial Services, Retailing and Government organisations. Steven is currently Head of Analytics at HML, the UK's largest provider of mortgage administration services. Previously he has worked as a data scientist, consultant and project manager for a variety of organizations in both the public and private sectors. Steven has a PhD in predictive analytics and is an Honorary Research Fellow in the Management Science Department at Lancaster University in the UK.

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Beschreibung

Produktdetails


Einband gebundene Ausgabe
Seitenzahl 260
Erscheinungsdatum 01.07.2014
Sprache Englisch
ISBN 978-1-137-37927-6
Reihe Business in the Digital Economy
Verlag Palgrave Macmillan
Maße (L/B/H) 151/217/20 mm
Gewicht 546
Auflage 2014
Buch (gebundene Ausgabe, Englisch)
Fr. 45.90
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Predictive Analytics, Data Mining and Big Data

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