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024 7 _a10.1007/978-3-031-48956-3
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024 8 _aCIPO000260734
035 _a(OCoLC)1430431939
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050 4 _aQ180.55.Q36
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100 1 _aIgual, Laura,
_eauthor.
245 1 0 _aIntroduction to data science :
_ba Python approach to concepts, techniques and applications /
_cLaura Igual, Santi Segu�i.
250 _aSecond edition.
264 1 _aCham :
_bSpringer,
_c2024.
300 _a1 online resource (xiv, 246 pages) :
_billustrations (some color)
336 _atext
_btxt
_2rdacontent.
337 _acomputer
_bc
_2rdamedia.
338 _aonline resource
_bcr
_2rdacarrier.
490 1 _aUndergraduate topics in computer science,
_x2197-1781.
504 _aIncludes bibliographical references and index.
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.
506 1 _aAccess limited to UNC Chapel Hill-authenticated users.
_fUnlimited simultaneous users.
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 Matem�atiques i Inform�atica, Universitat de Barcelona, Spain. Dr. Santi Segu�i is an Associate Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitri�a, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera.
588 0 _aOnline resource; title from PDF title page (SpringerLink, viewed April 18, 2024).
590 0 _aContent provider: SpringerLink.
650 0 _aPython (Computer program language)
650 0 _aQuantitative research.
650 7 _aData mining
_2fast.
650 7 _aMathematical statistics
_2fast.
650 7 _aPython (Computer program language)
_2fast.
650 7 _aQualitative research
_2fast.
700 1 _aSegu�i, Santi,
_eauthor.
773 0 _tSpringer e-books (online collection). Computer science. 2024.
773 1 _tOCLC WorldShare Collection Manager managed collection. SPRnew.
776 0 8 _cOriginal
_z3031489551
_z9783031489556
_w(OCoLC)1412623335.
776 0 8 _iPrint version :
_z9783031489556.
830 0 _aUndergraduate topics in computer science,
_x2197-1781.
856 4 0 _uhttps://login.libproxy.lib.unc.edu/login?url=https://link.springer.com/10.1007/978-3-031-48956-3
_yFull text available via the UNC-Chapel Hill Libraries
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