European University Institute Library

Mathematical underpinnings of analytics, theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford

eng

Includes bibliographical references (pages 251-258) and index

illustrations

index present

non fiction

Mathematical underpinnings of analytics

bibliography

892563228

Peter Grindrod, CBE, Mathematical Institute, University of Oxford

theory and applications

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.--, Provided by Publisher

Introduction: The Underpinnings of Analytics 1: Similarity, Graphs and Networks, Random Matrices and SVD 2: Dynamically Evolving Networks 3: Structure and Responsiveness 4: Clustering and Unsupervised Classication 5: Multiple Hypothesis Testing Over Live Data 6: Adaptive Forecasting 7: Customer Journeys and Markov Chains Appendix: Uncertainty, Probability and Reasoning