000			04302cam a22005895i 4500
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007			cr |||||||||||
008			170222s2017 gw |||| o |||| 0|eng
010			_a 2019744237
020			_a9783319500171
024	7		_a10.1007/978-3-319-50017-1 _2doi
035			_a(DE-He213)978-3-319-50017-1
040			_aDLC _beng _epn _erda _cDLC
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2bicssc
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223
100	1		_aIgual, Laura. _eauthor.
245	1	0	_aIntroduction to Data Science : _bA Python Approach to Concepts, Techniques and Applications / _cby Laura Igual, Santi Seguí.
250			_a1st ed. 2017.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2024
300			_a1 online resource (XIV, 218 pages 73 illustrations, 67 illustrations in color.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUndergraduate Topics in Computer Science, _x1863-7310
505	0		_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.
520			_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.
588			_aDescription based on publisher-supplied MARC data.
650		0	_aArtificial intelligence.
650		0	_aData mining.
650		0	_aMathematical statistics. _95882
650		0	_aPattern recognition.
650		0	_aStatistics.
650	1	4	_aData Mining and Knowledge Discovery. _0https://scigraph.springernature.com/ontologies/product-market-codes/I18030
650	2	4	_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650	2	4	_aPattern Recognition. _0https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
650	2	4	_aProbability and Statistics in Computer Science. _0https://scigraph.springernature.com/ontologies/product-market-codes/I17036
650	2	4	_aStatistics and Computing/Statistics Programs. _0https://scigraph.springernature.com/ontologies/product-market-codes/S12008
700	1		_aSeguí, Santi. _eauthor.
776	0	8	_iPrint version: _tIntroduction to data science. _z9783319500164 _w(DLC) 2016962046
776	0	8	_iPrinted edition: _z9783319500164
776	0	8	_iPrinted edition: _z9783319500188
830		0	_aUndergraduate Topics in Computer Science, _x1863-7310
906			_a0 _bibc _corigres _du _encip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c33819 _d33819