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024 7 _a10.1007/978-3-031-48956-3
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100 1 _aIgual, Laura.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100532
245 1 0 _aIntroduction to Data Science
_h[electronic resource] :
_bA Python Approach to Concepts, Techniques and Applications /
_cby Laura Igual, Santi Seguí.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXIV, 246 p. 82 illus., 78 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aUndergraduate Topics in Computer Science,
_x2197-1781
505 0 _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.
520 _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.
650 0 _aArtificial intelligence
_xData processing.
_921787
650 0 _aData mining.
_93907
650 0 _aPython (Computer program language).
_96666
650 0 _aArtificial intelligence.
_93407
650 1 4 _aData Science.
_934092
650 2 4 _aData Mining and Knowledge Discovery.
_9100534
650 2 4 _aPython.
_934340
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aSeguí, Santi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100536
710 2 _aSpringerLink (Online service)
_9100538
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aUndergraduate Topics in Computer Science,
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_9100540
856 4 0 _uhttps://doi.org/10.1007/978-3-031-48956-3
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