Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods (Business in the Digital Economy)

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en Limba Engleză Carte Hardback – July 2014
This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
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ISBN-13: 9781137379276
ISBN-10: 1137379278
Pagini: 248
Ilustrații: 9 b/w tables, 23 figures
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.55 kg
Ediția: 2014
Editura: Palgrave Macmillan UK
Colecția Palgrave Macmillan
Seria Business in the Digital Economy

Locul publicării: London, United Kingdom


1. Introduction 2. Using Predictive Models 3. Analytics, Organization and Culture 4. The Value of Data 5. Ethics and Legislation 6. Types of Predictive Models 7. The Predictive Analytics Process 8. How to Build a Predictive Model 9. Text Mining and Social Network Analysis 10. Hardware, Software and All That Jazz


'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

Notă biografică

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.


Broad, yet disciplined approach with extensive experience of private, academic, and government sectors the author is uniquely placed to offer valuable and relevant insights
The book will appeal to a broad range of people in many organizations as a practical guide, designed for managers and practitioners
Fresh perspective on Big Data: tracks the development of Big Data from its beginnings in the 1980s