Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise

Vaughan, Daniel

ISBN 10: 1492060941 / ISBN 13: 9781492060949

Editore: O'Reilly Media, 2020

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Dati bibliografici

• Titolo: Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise
• Editore: O'Reilly Media
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1492060941
• ISBN 13: 9781492060949
• Rilegatura: paperback
• Edizione: 1st Edition.
• Condizione: Very Good

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Riassunto

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs.

Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You'll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.

• Break business decisions into stages that can be tackled using different skills from the analytical toolbox
• Identify and embrace uncertainty in decision making and protect against common human biases
• Customize optimal decisions to different customers using predictive and prescriptive methods and technologies
• Ask business questions that create high value through AI- and data-driven technologies

Informazioni sull?autore

Daniel Vaughan is the data science lead for Airbnb in Latin America. Previously he was Head of Data Science and Chief Data Officer at Telefonica Mexico. He has more than 15 years of experience solving problems using predictive and prescriptive methods.