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OCoLC |
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20231120010429.0 |
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m o d |
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cr |n||||||||| |
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191202s2020 enk o 001 0 eng d |
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|a YDX
|b eng
|e pn
|c YDX
|d OPELS
|d OCLCF
|d N$T
|d OCLCQ
|d BWN
|d SFB
|d OCLCQ
|d OCLCO
|d K6U
|d OCLCQ
|d OCLCO
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| 019 |
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|a 1129399805
|a 1165833467
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| 020 |
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|a 9780128172582
|q (electronic bk.)
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| 020 |
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|a 0128172584
|q (electronic bk.)
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| 020 |
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|z 9780128155035
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| 020 |
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|z 0128155035
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| 035 |
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|a (OCoLC)1129274222
|z (OCoLC)1129399805
|z (OCoLC)1165833467
|
| 050 |
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4 |
|a TA1634
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| 082 |
0 |
4 |
|a 006.3/7
|2 23
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| 245 |
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|a Infrastructure computer vision /
|c edited by Ioannis Brilakis, Carl Haas.
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| 264 |
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1 |
|a Oxford :
|b Butterworth-Heinemann,
|c �2020.
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| 300 |
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|a 1 online resource
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 500 |
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|a Includes indexes.
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| 505 |
0 |
0 |
|t Introduction: Why you need to understand data analytics --
|t Section 1. Getting started: Keep up with your quants: an innumerate's guide to navigating big data /
|r by Thomas H. Davenport --
|t A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics /
|r by Thomas C. Redman --
|t Section 2. Gather the right information: Do you need all that data?: questions to ask for a focused search /
|r by Ron Ashkenas --
|t How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need /
|r by Michael Li, Madina Kassengaliyeva, and Raymond Perkins --
|t How to design a business experiment: tips for using the scientific method /
|r by Oliver Hauser and Michael Luca --
|t Know the difference between your data and your metrics: understand what you're measuring /
|r by Jeff Bladt and Bob Filbin --
|t The fundamentals of A/
|r B testing: how it works and mistakes to avoid /
|r by Amy Gallo --
|t Can your data be trusted?: gauge whether your data is safe to use /
|r by Thomas C. Redman --
|t Section 3. Analyze the data: A predictive analytics primer: look to the future by looking at the past /
|r by Thomas H. Davenport --
|t Understanding regression analysis: evaluate the relationship between variables /
|r by Amy Gallo --
|t When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong /
|r by David Ritter --
|t Can machine learning solve your business problem?: steps to take before investing in AI /
|r by Anastassia Fedyk --
|t A refresher on statistical significance: check if your results are real or just luck /
|r by Amy Gallo --
|t Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment /
|r by Bart de Langhe, Stefano Puntoni, and Richard Larrick --
|t Pitfalls of data-driven decisions: the cognitive traps to avoid /
|r by Megan MacGarvie and Kristina McElheran --
|t Don't let your analytics cheat the truth: always ask for the outliers /
|r by Michael Schrage --
|t Section 4. Communicate your findings: Data is worthless if you don't communicate it: tell people what it means /
|r by Thomas H. Davenport --
|t When data visualization works, and when it doesn't: not all data is worth the effort /
|r by Jim Stikeleather --
|t How to make charts that pop and persuade: questions to help give your numbers meaning /
|r by Nancy Duarte --
|t Why it's so hard for us to communicate uncertainty: illustrating --
|t and understanding --
|t the likelihood of events: an interview with Scott Berinato /
|r by Nicole Torres --
|t Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally /
|r by Jon M. Jachimowicz --
|t Decisions don't start with data: influence others through story and emotion /
|r by Nick Morgan.
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| 588 |
0 |
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|a Print version record.
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| 650 |
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0 |
|a Computer vision.
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| 650 |
|
6 |
|a Vision par ordinateur.
|0 (CaQQLa)201-0074889
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| 650 |
|
7 |
|a Computer vision
|2 fast
|0 (OCoLC)fst00872687
|
| 700 |
1 |
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|a Brilakis, Ioannis.
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| 700 |
1 |
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|a Haas, Carl.
|
| 776 |
0 |
8 |
|i Print version:
|z 0128155035
|z 9780128155035
|w (OCoLC)1097677784
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128155035
|z Texto completo
|
