LEADER 00951nam0-22003251i-450 001 990004595820403321 005 20170926150550.0 010 $a91-7346-041-9 035 $a000459582 035 $aFED01000459582 035 $a(Aleph)000459582FED01 035 $a000459582 100 $a19990530d1977----km-y0itay50------ba 101 0 $aeng$alat 102 $aSE 105 $ay-------001yy 200 1 $aMiscellanea propertiana$fErik Wistrand 210 $aGoeteborg$cActa Universitatis Gothoburgensis$d1977 215 $a84 p.$d22 cm 225 1 $aStudia Graeca et Latina Gothoburgensia$v38 610 0 $aProperzio, Sesto$aOpere 676 $a874.01$v21$zita 700 1$aWistrand,$bErik$f<1907-1998>$0182260 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004595820403321 952 $aP2B 650 PROPERTIUS 8W.E. 1977$bbibl.51163$fFLFBC 959 $aFLFBC 996 $aMiscellanea propertiana$9550468 997 $aUNINA LEADER 04903nam 22006255 450 001 9910416083303321 005 20251113192307.0 010 $a3-030-45574-2 024 7 $a10.1007/978-3-030-45574-3 035 $a(CKB)4100000011372956 035 $a(DE-He213)978-3-030-45574-3 035 $a(MiAaPQ)EBC6284441 035 $a(Au-PeEL)EBL6284441 035 $a(OCoLC)1183957807 035 $a(PPN)260302899 035 $a(MiAaPQ)EBC30766836 035 $a(Au-PeEL)EBL30766836 035 $a(EXLCZ)994100000011372956 100 $a20200806d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGuide to Intelligent Data Science $eHow to Intelligently Make Use of Real Data /$fby Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo 205 $a2nd ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 420 p. 179 illus., 122 illus. in color.) 225 1 $aTexts in Computer Science,$x1868-095X 311 08$a3-030-45573-4 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 410 0$aTexts in Computer Science,$x1868-095X 606 $aData mining 606 $aMachine learning 606 $aQuantitative research 606 $aData Mining and Knowledge Discovery 606 $aMachine Learning 606 $aData Analysis and Big Data 615 0$aData mining. 615 0$aMachine learning. 615 0$aQuantitative research. 615 14$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 615 24$aData Analysis and Big Data. 676 $a006.3 700 $aBerthold$b M$g(Michael),$0133096 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910416083303321 996 $aGuide to Intelligent Data Science$91891911 997 $aUNINA