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  • 1
    Online Resource
    Online Resource
    2015-
    Title: The journal of finance and data science
    Year of publication: 2015-
    Document type: Online Resource
    Link(s): Fulltext
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  • 2
    Online Resource
    Online Resource
    Second edition 2020
    ISBN: 9781108564175  ISBN: 9781108473989 (hardback) 
    Title: Data mining and machine learning
    Subtitle: fundamental concepts and algorithms
    Edition: Second edition
    Publisher(s): Cambridge University Press
    Year of publication: 2020
    Document type: Online Resource
    Physical description: 1 online resource (xii, 766 pages)
    ISBN: 9781108564175 , 9781108473989 (hardback)
    Terms of use: Vorläufiger Zugriff bis zum Jahresende
    Subject(s): Data mining
    Abstract: The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning
    Link(s): Fulltext
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  • 3
    Online Resource
    Online Resource
    1st ed. 2020 2020
    ISBN: 978-3-030-46161-4 
    Title: Contemporary Experimental Design, Multivariate Analysis and Data Mining
    Subtitle: Festschrift in Honour of Professor Kai-Tai Fang
    Edition: 1st ed. 2020
    Publisher(s): Cham : Springer International Publishing | Imprint: Springer
    Document type: Online Resource
    Physical description: 1 Online-Ressource (XVII, 386 p. 42 illus., 30 illus. in color)
    ISBN: 978-3-030-46161-4
    Identifier: 10.1007/978-3-030-46161-4
    Parallel edition(s): Printed edition: 978-3-030-46160-7
    Printed edition: 978-3-030-46162-1
    Printed edition: 978-3-030-46163-8
    Footnote: Vorläufiger Zugriff bis zum Jahresende
    Link(s): Fulltext
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  • 4
    Online Resource
    Online Resource
    2020
    ISBN: 978-3-446-45717-1 
    Title: Von Data Mining bis Big Data
    Subtitle: Handbuch für die industrielle Praxis
    Publisher(s): München : Hanser
    Document type: Online Resource
    Physical description: 1 Online-Ressource (300 Seiten) Illustrationen
    ISBN: 978-3-446-45717-1
    Identifier: 10.3139/9783446457171
    Parallel edition(s): Erscheint auch als: Druck-Ausgabe: 978-3-446-45550-4
    Link(s): Fulltext
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  • 5
    Online Resource
    Online Resource
    2nd ed. 2020 2020
    ISBN: 978-981-13-9664-9 
    Title: Predictive Data Mining Models
    Edition: 2nd ed. 2020
    Publisher(s): Singapore : Springer Singapore | Imprint: Springer
    Document type: Online Resource
    Physical description: 1 Online-Ressource (XI, 125 p. 77 illus., 69 illus. in color)
    Collective title(s): 2191-1436
    Computational Risk Management
    ISBN: 978-981-13-9664-9
    Identifier: 10.1007/978-981-13-9664-9
    Parallel edition(s): Printed edition: 978-981-13-9663-2
    Printed edition: 978-981-13-9665-6
    Printed edition: 978-981-13-9666-3
    Footnote: Vorläufiger Zugriff bis zum Jahresende
    Link(s): Fulltext
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  • 6
    Online Resource
    Online Resource
    1st ed. 2020 2020
    ISBN: 978-3-030-33416-1 
    Title: Statistical Modeling in Biomedical Research
    Subtitle: Contemporary Topics and Voices in the Field
    Edition: 1st ed. 2020
    Publisher(s): Cham : Springer International Publishing | Imprint: Springer
    Document type: Online Resource
    Physical description: 1 Online-Ressource (XVIII, 491 p. 107 illus., 79 illus. in color)
    Collective title(s): 2524-7735
    Emerging Topics in Statistics and Biostatistics
    ISBN: 978-3-030-33416-1
    Identifier: 10.1007/978-3-030-33416-1
    Parallel edition(s): Printed edition: 978-3-030-33415-4
    Printed edition: 978-3-030-33417-8
    Printed edition: 978-3-030-33418-5
    Footnote: Vorläufiger Zugriff bis zum Jahresende
    Link(s): Fulltext
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  • 7
    Online Resource
    Online Resource
    2020
    ISBN: 9781119544463  ISBN: 1119544467  ISBN: 9781119544449  ISBN: 1119544440 
    Contributer: Gupta, Deepak
    Title: Intelligent data analysis
    Subtitle: from data gathering to data comprehension
    Publisher(s): Wiley
    Year of publication: 2020
    Document type: Online Resource
    ISBN: 9781119544463 , 1119544467 , 9781119544449 , 1119544440
    Terms of use: Vorläufiger Zugriff bis Ende November 2020
    Abstract: "The new tool for analyses is ?Intelligent Data Analysis (IDA)?. IDA can be defined as the use of specialized statistical, pattern recognition, machine learning, data abstraction, and visualization tools for analysis of data and discovery of mechanisms that created the data. Such data are typically complex, meaning that they are characterized by many records, many variables, subtle interactions between variables, or a combination of all three. Engineering, computing sciences, database science, machine learning, and even artificial intelligence are bringing their powers to this newly born data analysis discipline. The main idea underlying the concept of Intelligent Data Analysis is extracting knowledge from a very large amount of data, with a very large amount of variables; data that represents very complex, non-linear, real-life problems. Moreover, IDA can help when starting from the raw data, coping with prediction tasks without knowing the theoretical description of the underlying process, classification tasks of new events based on past ones, or modeling the aforementioned unknown process. Classification, prediction, and modeling are the cornerstones that Intelligent Data Analysis can bring to us"--
    Link(s): Fulltext
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  • 8
    Online Resource
    Online Resource
    2020
    ISBN: 9780691198859 
    Person(s): Hand, David J.
    Title: Dark Data
    Subtitle: Why What You Don’t Know Matters
    Publisher(s): Princeton University Press
    Year of publication: 2020
    Document type: Online Resource
    ISBN: 9780691198859
    Terms of use: Vorläufiger Zugriff bis zum Jahresende
    Abstract: A practical guide to making good decisions in a world of missing dataIn the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see.Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions.Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones
    Link(s): Fulltext
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  • 9
    Contributer: China Science Publishing & Media Ltd.
    Title: Data Structures and Algorithms Analysis
    Publisher(s): De Gruyter
    Year of publication: 2020
    Document type: Online Resource
    ISBN: 9783110676075
    Terms of use: Vorläufiger Zugriff bis zum Jahresende
    Abstract: The systematic description starts with basic theory and applications of different kinds of data structures, including storage structures and models. It also explores on data processing methods such as sorting, index and search technologies. Due to its numerous exercises the book is a helpful reference for graduate students, lecturers
    Link(s): Fulltext
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  • 10
    Contributer: China Science Publishing & Media Ltd.
    Title: Data Structures and Algorithms Analysis
    Publisher(s): De Gruyter
    Year of publication: 2020
    Document type: Online Resource
    ISBN: 9783110595581
    Terms of use: Vorläufiger Zugriff bis zum Jahresende
    Abstract: Data structures is a key course for computer science and related majors. This book presents a variety of practical or engineering cases and derives abstract concepts from concrete problems. Besides basic concepts and analysis methods, it introduces basic data types such as sequential list, tree as well as graph. This book can be used as an undergraduate textbook, as a training textbook or a self-study textbook for engineers
    Link(s): Fulltext
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