000			03571cam a22005897i 4500
999			_c12351 _d12351
001			20748507
005			20200224110340.0
008			181119s2018 cc a b 001 0 eng d
010			_a 2018304426
015			_aGBB704535 _2bnb
016	7		_a018171667 _2Uk
020			_a9781491928462
035			_a(OCoLC)on1019927056
040			_aCCE _beng _cCCE _erda _dOCLCO _dFBR _dOCLCO _dNZAUC _dOCLCF _dYDX _dCLE _dOCL _dCHVBK _dIAX _dOCLCO _dPAU _dUWO _dSOI _dOCLCO _dCOD _dOCLCO _dTME _dOCLCQ _dZHC _dIGA _dOCLCO _dINTCL _dOCLCO _dK6U _dOCLCO _dUKMGB _dTCJ _dOCLCO _dDLC _dEG-NcFUE
042			_alccopycat
050	0	0	_aQA76.9.I52 _bF57 2018
082	0	0	_223 _a005.74 _bF.D.M
100	1		_aFisher, Danyel, _d1975- _eauthor.
245	1	0	_aMaking data visual : _ba practical guide to using visualization for insight / _cDanyel Fisher and Miriah Meyer.
250			_aFirst edition.
264		1	_aBeijing : _bO'Reilly Media, _c[2018]
264		4	_cc2018
300			_axiii, 149 pages : _billustrations (chiefly color) ; _c23 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aPreface -- Getting to an effective visualization -- From questions to tasks -- Data counseling, exploration, and prototyping -- Components of a visualization -- Single views -- Multiple and coordinated views -- Case study 1: Visualizing telemetry to improve software -- Case study 2: Visualizing biological data -- Conclusions.
520			_a"You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you're stumped as to how to turn that data into insight. Which part of that data actually matters, and where is this insight hidden? If you're a data scientist who struggles to navigate the murky space between data and insight, this book will help you think about and reshape data for visual data exploration. It's ideal for relatively new data scientists, who may be computer-knowledgeable and data-knowledgeable, but do not yet know how to create effective, explorable representations of data. With this book, you'll learn: Task analysis, driven by a series of leading questions that draw out the important aspects of the data to be explored; Visualization patterns, each of which take a different perspective on data and answer different questions; A taxonomy of visualizations for common data types; Techniques for gathering design requirements; When and where to make use of statistical methods."-- _cProvided by publisher.
650		0	_aInformation visualization.
650		0	_aData mining.
650		0	_aVisualization _xData processing.
650		0	_aDatabases.
650	1	2	_aInformation Systems.
650	1	2	_aElectronic Data Processing.
650	2	2	_aData Display.
650	2	2	_aComputer Communication Networks.
650		7	_aCOMPUTERS / Data Processing. _2bisacsh
650		7	_aCOMPUTERS / Data Visualization. _2bisacsh
650		7	_aCOMPUTERS / Databases / Data Mining. _2bisacsh
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aDatabases. _2fast _0(OCoLC)fst00888065
650		7	_aInformation visualization. _2fast _0(OCoLC)fst00973185
650		7	_aVisualization _xData processing. _2fast _0(OCoLC)fst01168123
650		7	_aDaten _2gnd
650		7	_aVisualisierung _2gnd
700	1		_aMeyer, Miriah, _eauthor.
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK