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001 978-3-319-55444-0
003 DE-He213
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008 170701s2017 gw | s |||| 0|eng d
020 _a9783319554440
_9978-3-319-55444-0
024 7 _a10.1007/978-3-319-55444-0
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aSkiena, Steven S.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 4 _aThe Data Science Design Manual
_h[electronic resource] /
_cby Steven S. Skiena.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVII, 445 p. 180 illus., 137 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTexts in Computer Science,
_x1868-0941
505 0 _aWhat is Data Science? -- Mathematical Preliminaries -- Data Munging -- Scores and Rankings -- Statistical Analysis -- Visualizing Data -- Mathematical Models -- Linear Algebra -- Linear and Logistic Regression -- Distance and Network Methods -- Machine Learning -- Big Data: Achieving Scale.
520 _aThis engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com).
650 0 _aData mining.
650 0 _aPattern recognition.
650 0 _aBig data.
650 0 _aMathematics.
650 0 _aVisualization.
650 0 _aStatistics .
650 1 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
650 2 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
650 2 4 _aBig Data/Analytics.
_0http://scigraph.springernature.com/things/product-market-codes/522070
650 2 4 _aVisualization.
_0http://scigraph.springernature.com/things/product-market-codes/M14034
650 2 4 _aStatistics and Computing/Statistics Programs.
_0http://scigraph.springernature.com/things/product-market-codes/S12008
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319554433
776 0 8 _iPrinted edition:
_z9783319554457
776 0 8 _iPrinted edition:
_z9783319856636
830 0 _aTexts in Computer Science,
_x1868-0941
856 4 0 _uhttps://doi.org/10.1007/978-3-319-55444-0
912 _aZDB-2-SCS
999 _c17841
_d17841
942 _cebook