A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Infrastructure computer vision
Introduction: Why you need to understand data analytics --Section 1. Getting 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 --Section 2. 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 --Section 3. 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 --Section 4. 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.
Infrastructure computer vision
Introduction: Why you need to understand data analytics --Section 1. Getting 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 --Section 2. 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 --Section 3. 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 --Section 4. 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.
Infrastructure computer vision
Haas, Carl (editor) / Brilakis, Ioannis (editor)
2020
1 Online-Ressource (xvii, 389 Seiten)
Illustrationen
Includes indexes
Book
Electronic Resource
English
Infrastructure computer vision
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