03944nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050000900172072001500181072002300196072001400219100008200233245015200315250001800467264007500485300006500560336002600625337002600651338003600677347002400713490005700737505035500794520163901149650004602788650002302834650004702857650003602904650001802940650004102958650001902999650003603018700008403054710003403138773002603172776003603198776003603234830006403270856004603334912001403380912001503394999001703409978-3-031-48956-3DE-He21320251030111815.0cr nn 008mamaa240412s2024    sz |    s    |||| 0|eng d  a97830314895639978-3-031-48956-37 a10.1007/978-3-031-48956-32doi 4aQ336 7aUN2bicssc 7aCOM0210002bisacsh 7aUN2thema1 aIgual, Laura.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97506410aIntroduction to Data Scienceh[electronic resource] :bA Python Approach to Concepts, Techniques and Applications /cby Laura Igual, Santi Seguí.  a2nd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXIV, 246 p. 82 illus., 78 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUndergraduate Topics in Computer Science,x2197-17810 a1. Introduction to Data Science -- 2. Toolboxes for Data Scientists -- 3. Descriptive statistics -- 4. Statistical Inference -- 5. Supervised Learning -- 6. Regression Analysis -- 7. Unsupervised Learning -- 8. Network Analysis -- 9. Recommender Systems -- 10. Statistical Natural Language Processing for Sentiment Analysis -- 11. Parallel Computing.  aThis textbook presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or 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 concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science 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 Associate Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera. 0aArtificial intelligencexData processing. 0aData mining.96941 0aPython (Computer program language).964608 0aArtificial intelligence.95844114aData Science.24aData Mining and Knowledge Discovery.24aPython.94755324aArtificial Intelligence.9584411 aSeguí, Santi.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9750652 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303148955608iPrinted edition:z9783031489570 0aUndergraduate Topics in Computer Science,x2197-178196649440uhttps://doi.org/10.1007/978-3-031-48956-3  aZDB-2-SCS  aZDB-2-SXCS  c59088d59088