LEADER 04374nam 22007335 450 001 9910254840103321 005 20230810132106.0 010 $a3-319-50017-1 024 7 $a10.1007/978-3-319-50017-1 035 $a(CKB)3710000001080139 035 $a(DE-He213)978-3-319-50017-1 035 $a(MiAaPQ)EBC6314089 035 $a(MiAaPQ)EBC5596128 035 $a(Au-PeEL)EBL5596128 035 $a(OCoLC)974441700 035 $a(PPN)198869770 035 $a(EXLCZ)993710000001080139 100 $a20170222d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntroduction to Data Science $eA Python Approach to Concepts, Techniques and Applications /$fby Laura Igual, Santi Seguí 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 218 p. 73 illus., 67 illus. in color.) 225 1 $aUndergraduate Topics in Computer Science,$x2197-1781 311 $a3-319-50016-3 327 $aIntroduction to Data Science -- Toolboxes for Data Scientists -- Descriptive statistics -- Statistical Inference -- Supervised Learning -- Regression Analysis -- Unsupervised Learning -- Network Analysis -- Recommender Systems -- Statistical Natural Language Processing for Sentiment Analysis -- Parallel Computing. 330 $aThis accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution. 410 0$aUndergraduate Topics in Computer Science,$x2197-1781 606 $aData mining 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aMathematical statistics$xData processing 606 $aData Mining and Knowledge Discovery 606 $aProbability and Statistics in Computer Science 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 606 $aStatistics and Computing 615 0$aData mining. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 0$aMathematical statistics$xData processing. 615 14$aData Mining and Knowledge Discovery. 615 24$aProbability and Statistics in Computer Science. 615 24$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 615 24$aStatistics and Computing. 676 $a001.42 700 $aIgual$b Laura$4aut$4http://id.loc.gov/vocabulary/relators/aut$01061093 702 $aSeguí$b Santi$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254840103321 996 $aIntroduction to Data Science$92517495 997 $aUNINA