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999 _c41738
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003 BD-DhAAL
005 20191208162458.0
008 191118t2017 gw | o |||| 0|eng d
020 _a9783319554433 (print)
020 _a9783319554440
024 7 _a10.1007/978-3-319-55444-0
_2doi
035 _a(WaSeSS)OCM1ssj0001846412
035 _a10029592
035 _a(OCoLC)992989152
040 _dWaSeSS
_beng
_dNIC
_dBD-DhAAL
050 4 _aQA76.9.D343
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aSkiena, Steven S.
_937243
245 1 4 _aThe Data Science Design Manual /
_cSteven S. Skiena.
260 _aCham, Switzerland :
_bSpringer,
_cc2017
300 _axvii, 445 pages :
_billustrations ;
_c24 cm
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.
506 _aLicense restrictions may limit access.
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 (3)4z(BIntroduction to Data Science(3)4y (Bcourse. 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 (3)4z(BWar Stories,(3)4y (Boffering perspectives on how data science applies in the real world Includes (3)4z(BHomework Problems,(3)4y (Bproviding 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 (3)4z(BTake-Home Lessons,(3)4y (Bemphasizing the big-picture concepts to learn from each chapter Recommends exciting (3)4z(BKaggle Challenges(3)4y (Bfrom the online platform Kaggle Highlights (3)4z(BFalse Starts,(3)4y (Brevealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show (3)4z(BThe Quant Shop(3)4y ((Bwww.quant-shop.com).
526 _aCSE
650 0 _aComputer science.
_937244
650 0 _aBig data.
_937245
650 0 _aData mining.
_937246
650 0 _aPattern recognition.
_937247
650 0 _aMathematics.
_937248
650 0 _aVisualization.
_937249
650 0 _aStatistics.
_937250
650 1 4 _aComputer Science.
_937251
650 2 4 _aData Mining and Knowledge Discovery.
_937252
650 2 4 _aPattern Recognition.
_937253
650 2 4 _aBig Data/Analytics.
_937254
650 2 4 _aVisualization.
_937249
650 2 4 _aStatistics and Computing/Statistics Programs.
_937255
650 7 _aMathematical statistics
_xData processing.
_2fast
_937256
710 2 _aSpringerLink (Online service)
_937257
830 0 _aTexts in Computer Science,
_937258
852 _aAyesha Abed Library
_cGeneral Stacks
942 _2ddc
_cBK