The Resource Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford
Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford
Resource Information
The item Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
- Summary
- 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.--
- Language
- eng
- Edition
- First edition.
- Extent
- xiii, 261 pages
- Contents
-
- 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
- Isbn
- 9780198725091
- Label
- Mathematical underpinnings of analytics : theory and applications
- Title
- Mathematical underpinnings of analytics
- Title remainder
- theory and applications
- Statement of responsibility
- Peter Grindrod, CBE, Mathematical Institute, University of Oxford
- Language
- eng
- Summary
- 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.--
- Assigning source
- Provided by Publisher
- Cataloging source
- YDXCP
- http://library.link/vocab/creatorName
- Grindrod, Peter
- Dewey number
- 006.31
- Illustrations
- illustrations
- Index
- index present
- Literary form
- non fiction
- Nature of contents
- bibliography
- http://library.link/vocab/subjectName
-
- Data mining
- Mathematical analysis
- Big data
- Label
- Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford
- Bibliography note
- Includes bibliographical references (pages 251-258) and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent.
- Contents
- 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
- Control code
- FIEb17785443
- Dimensions
- 25 cm.
- Edition
- First edition.
- Extent
- xiii, 261 pages
- Isbn
- 9780198725091
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- System control number
- (OCoLC)892563228
- Label
- Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford
- Bibliography note
- Includes bibliographical references (pages 251-258) and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent.
- Contents
- 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
- Control code
- FIEb17785443
- Dimensions
- 25 cm.
- Edition
- First edition.
- Extent
- xiii, 261 pages
- Isbn
- 9780198725091
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- System control number
- (OCoLC)892563228
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Mathematical-underpinnings-of-analytics--theory/hPTs_nOV2Yw/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Mathematical-underpinnings-of-analytics--theory/hPTs_nOV2Yw/">Mathematical underpinnings of analytics : theory and applications, Peter Grindrod, CBE, Mathematical Institute, University of Oxford</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
