000			03254aaa a2200265 i 4500
999			_c10588 _d10588
005			20200706121800.0
008			200127t2017 xxu||||g |||| 00| 0 eng d
020			_a9781633430273 _a1633430278
040			_aUISEK-EC _bspa _erda
100	1		_98870 _aGodsey, Brian _eaut
245	1	0	_aThink like a data scientist : _btackle the data science process step-by-step / _cBrian Godsey
264		1	_aNew York _bManning Publications, _c 2017.
300			_a328 páginas, _c24 cm
336			_atxt
337			_2rdamedia _an
338			_2rdacarrier _anc
505			_aPreparing and gathering data and knowledge - Philosophies of data science - Setting goals by asking good questions - Data all around us: the virtual wilderness - Data wrangling: from capture to domestication - Data assessment: poking and prodding - Building a product with software and statistics - Developing a plan - Statistics and modeling: concepts and foundations - Software: statistics in action - Supplementary software: bigger, faster, more efficient - Plan execution: putting it all together - Finishing off the product and wrapping up - Delivering a product - After product delivery: problems and revisions - Wrapping up: putting the project away.
520			_aThink Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups.
526			_aM. Sistemas de Información mención en Data Science
082	0	4	_a004.6 _bG589t 2017
650	1	7	_2unescot _94868 _aComunicaciones de datos
650	2	7	_2unescot _91295 _aIngeniería de software
650	2	7	_2unescot _94195 _aSistemas de software
942			_cBK