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020 _a9783030455743
_9978-3-030-45574-3
024 7 _a10.1007/978-3-030-45574-3
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
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_2thema
082 0 4 _a006.312
_223
100 1 _aBerthold, Michael R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGuide to Intelligent Data Science
_h[electronic resource] :
_bHow to Intelligently Make Use of Real Data /
_cby Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo.
250 _a2nd ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXIII, 420 p. 179 illus., 122 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTexts in Computer Science,
_x1868-095X
505 0 _aIntroduction -- Practical Data Analysis: An Example -- Project Understanding -- Data Understanding -- Principles of Modeling -- Data Preparation -- Finding Patterns -- Finding Explanations -- Finding Predictors -- Evaluation and Deployment -- The Labelling Problem -- Appendix A: Statistics -- Appendix B: KNIME.
520 _aMaking 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 the subject. Prof. Dr. Michael R. Berthold is Professor for Bioinformatics and Information Mining at the University of Konstanz. Prof. Dr. Christian Borgelt is Professor for Data Science at the Paris Lodron University of Salzburg. Prof. Dr. Frank Höppner is Professor of Information Engineering at Ostfalia University of Applied Sciences. Prof. Dr. Frank Klawonn is Professor for Data Analysis and Pattern Recognition at the same institution and head of the Biostatistics Group at the Helmholtz Centre for Infection Research. Dr. Rosaria Silipo is a Principal Data Scientist and Head of Evangelism at KNIME AG.
650 0 _aData mining.
650 0 _aMachine learning.
650 0 _aQuantitative research.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMachine Learning.
650 2 4 _aData Analysis and Big Data.
700 1 _aBorgelt, Christian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aHöppner, Frank.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aKlawonn, Frank.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aSilipo, Rosaria.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030455736
776 0 8 _iPrinted edition:
_z9783030455750
776 0 8 _iPrinted edition:
_z9783030455767
830 0 _aTexts in Computer Science,
_x1868-095X
856 4 0 _uhttps://doi.org/10.1007/978-3-030-45574-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185292
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