Mathematical Underpinnings of Analytics: Theory and Applications

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Peter Grindrod (Professor of Mathematics, Professor of Mathematics, Mathematical Institute, University of Oxford)

Mathematical Underpinnings of Analytics

Theory and Applications

Peter Grindrod (Professor of Mathematics, Professor of Mathematics, Mathematical Institute, University of Oxford)

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English

Oxford University Press
30 October 2014

Combinatorics & graph theory; Applied mathematics; Mathematical modelling; Computer science

Analytics is the application of mathematical and statistical concepts to large data sets so as to distil insights that offer the owner some options for action and competitive advantage or value. This makes it the most desirable and valuable part of big data science.

Driven by the increased data capture from digital platforms, commercial fields are becoming data rich and analytics is growing in many sectors. This book presents analytics within a framework of mathematical theory and concepts building upon firm theory and foundations of probability theory, graphs and networks, random matrices, linear algebra, optimization, forecasting, discrete dynamical systems, and more. Following on from the theoretical considerations, applications are given to data from commercially relevant interests: supermarket baskets; loyalty cards; mobile phone call records; smart meters; 'omic' data; sales promotions; social media; and microblogging. Each chapter tackles a topic in analytics: social networks and digital marketing; forecasting; clustering and segmentation; inverse problems; Markov models of behavioural changes; multiple hypothesis testing and decision-making; and so on. Chapters start with background mathematical theory explained with a strong narrative and then give way to practical considerations and then to exemplar applications. Exercises (and solutions), external data resources, and suggestions for project work are given. The book includes an appendix giving a crash course in Bayesian reasoning, for both ease and completeness.

By: Peter Grindrod (Professor of Mathematics Professor of Mathematics Mathematical Institute University of Oxford)
Imprint: Oxford University Press
Country of Publication: United Kingdom
Dimensions: Height: 240mm, Width: 162mm, Spine: 23mm
Weight: 1g
ISBN: 9780198725091
ISBN 10: 0198725094
Pages: 276
Publication Date: 30 October 2014
Audience: College/higher education , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Peter Grindrod researches a range of topics in analytics for customer-facing industries and in particular for the digital society. He is in an almost unique position of having experience within commercial settings as well as within academia. He is a former President of the Institute of Mathematics and its Applications, member of the EPSRC and Chair of the EPSRC's User Panel. He authored Patterns and Waves (OUP 1991, 2nd edn 1996) and has been awarded a CBE for his contribution to mathematics R&D. In 1998 he was co-founder and Technical Director of a start-up company, Numbercraft Limited, supplying analytics services and software to retailers and consumer goods manufacturers. He is a co-founder of Cignifi Inc, a Boston-based company that uses mobile phone records to provide behaviour based credit referencing for pre pay customers in emerging economies. He is a founder of Counting Lab Ltd, a UK-based start-up translating state of the art mathematics into prototype products and services.

Reviews for Mathematical Underpinnings of Analytics: Theory and Applications

It is an essential read for any mathematician * Alan Stevens, Mathematics Today * There is a great need for this book, which connects the typical analytical problems that organizations face with the underlying maths and statistics to solve them. Grindrod communicates his deep expertise ... for executives who want to understand the math underlying their businesses, and the quant geeks who want to know the business problems they are capable of addressing * Thomas H. Davenport, Babson College, MIT Fellow, Author of Competing on Analytics and Big Data @ Work *