Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods

Finlay, Steven

ISBN 10: 1137379278 / ISBN 13: 9781137379276

Edité par Palgrave Macmillan, 2014

A propos de cet article

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. Series: Business in the Digital Economy. Num Pages: 260 pages, 9 black & white tables, 23 figures. BIC Classification: KJC; KJF; KJQ; KJU. Category: (G) General (US: Trade). Dimension: 151 x 217 x 20. Weight in Grams: 454. . 2014. Hardcover. . . . .

N° de réf. du vendeur V9781137379276

Détails bibliographiques

• Titre: Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods
• Éditeur: Palgrave Macmillan
• Date de parution: 2014
• Langue: anglais
• ISBN à 10 chiffres: 1137379278
• ISBN à 13 chiffres: 9781137379276
• Reliure: Couverture rigide
• État de l'article: New

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.