9783030455736 - Guide to Intelligent Data Science: How to Intelligently Make Use of Real Data di Berthold, Michael R.; Borgelt, Christian; Hppner, Frank; Klawonn, Frank; Silipo, Ro... (18 risultati)

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. 2nd ed. 2020.

Condizione: New.

Condizione: New. In.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New.

Condizione: New.

Condizione: New.

Hardback. Condizione: New. 2nd ed. 2020.

Hardcover. Condizione: New. New. book.

Hardcover. Condizione: Brand New. 2nd edition. 420 pages. 9.25x6.00x1.00 inches. In Stock.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitledGuide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a 'need-to-have' tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a 'need to use, need to keep' resource following one's exploration of thesubject.

Condizione: new. Questo è un articolo print on demand.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitledGuide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a 'need-to-have' tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a 'need to use, need to keep' resource following one's exploration of the subject. 436 pp. Englisch.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Supplies a broad-range of perspectives on data science, providing readers with a comprehensive account of the fieldPresents a focus on practical aspects, in addition to a detailed description of the theoryEmphasizes the common pitfalls that.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a 'need-to-have' tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a 'need to use, need to keep' resource following one's exploration of thesubject.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 436 pp. Englisch.