000			03575nam a22002657a 4500
005			20210723170342.0
008			210723b ck ||||| |||| 00| 0 eng d
020			_a9781633694286
040			_aCO-BoUGC _cCO-BoUGC
041			_aInglés _hInglés
082			_a658.4038 _bH339
110			_aHarvard Bussiness Review Press _cBoston, Massachusetts _9384180
245			_aHBR Guide to. Data analytics basics for managers
250			_a1a ed.
260			_aBoston _bHarvard Business Review Press _c2018
300			_a231 pág. _c23 cm. x 13 cm.
505			_aGetting started: Keep up with your quants: an innumerate's guide to navigating big data / by Thomas H. Davenport. -- A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics / by Thomas C. Redman.-- Gather the right information: Do you need all that data?: questions to ask for a focused search / by Ron Ashkenas.-- How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins.-- How to design a business experiment: tips for using the scientific method / by Oliver Hauser and Michael Luca.-- Know the difference between your data and your metrics: understand what you're measuring / by Jeff Bladt and Bob Filbin.-- The fundamentals of A/B testing: how it works and mistakes to avoid / by Amy Gallo.-- Can your data be trusted?: gauge whether your data is safe to use / by Thomas C. Redman.-- Analyze the data: A predictive analytics primer: look to the future by looking at the past / by Thomas H. Davenport.-- Understanding regression analysis: evaluate the relationship between variables / by Amy Gallo.-- When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong / by David Ritter.-- Can machine learning solve your business problem?: steps to take before investing in AI / by Anastassia Fedyk.-- A refresher on statistical significance: check if your results are real or just luck / by Amy Gallo.-- Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick.-- Pitfalls of data-driven decisions: the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran.-- Don't let your analytics cheat the truth: always ask for the outliers / by Michael Schrage.-- Communicate your findings: Data is worthless if you don't communicate it: tell people what it means / by Thomas H. Davenport.-- When data visualization works, and when it doesn't: not all data is worth the effort / by Jim Stikeleather.-- How to make charts that pop and persuade: questions to help give your numbers meaning / by Nancy Duarte.-- Why it's so hard for us to communicate uncertainty: illustrating.- and understanding.- the likelihood of events: an interview with Scott Berinato / by Nicole Torres.-- Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz.-- Decisions don't start with data: influence others through story and emotion / by Nick Morgan
650			_2LEMB _aGESTIÓN DE LA INFORMACIÓN _9384181
650			_aSISTEMAS DE INFORMACIÓN EN LOS NEGOCIOS _2LEMB _9332318
650			_aNEGOCIOS POR INTERNET _2LEMB _9151516
650			_aANÁLISIS DE REGRESIÓN _2LEMB
650			_aGESTIÓN _xMÉTODOS ESTADÍSTICOS _2LEMB _9384182
650			_aTOMA DE DECISIONES _xMÉTODOS ESTADÍSTICOS _2LEMB _9384183
942			_2ddc _cBK _n0
999			_c254284 _d254284