You're reading from Principles of Strategic Data Science

Product typeBook

Published inJun 2019

Reading LevelExpert

Publisher

ISBN-139781838985295

Edition1st Edition

Languages

Python

Concepts

Data Science

Author (1)

Peter Prevos

References

Anderson, C. (2015). Creating a Data-Driven Organization: Practical Advice from the Trenches. Sebastopol, CA: O'Reilly Media Inc.

Bennett, C.M., Baird, A.A., Miller, M.B., & Wolford, G.L. (2009). Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction. NeuroImage, 47, S125. https://doi.org/10.1016/S1053-8119(09)71202-9.

Bryman, A., & Bell, E. (2011). Ethics in Business Research. In Business Research Methods (3rd ed.). Oxford: Oxford University Press.

Burkov, A. (2019). The Hundred-Page Machine Learning Book. LeanPub.

Caffo, B., Peng, R., & Leek, J.T. (2018). Executive Data Science. A Guide to Training and Managing the Best Data Scientists. LeanPub.

Carretero, S., Vuorikari, R., Punie, Y., European Commission, & Joint Research Centre. (2017). DigComp 2.1 The Digital Competence Framework for Citizens with Eight Proficiency Levels and Examples of Use.

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Author (1)

Peter Prevos

Dr Peter Prevos is a civil engineer and social scientist who also dabbles in theatrical magic. Peter has almost three decades of experience as a water engineer and manager, working in Europe, Africa, Asia, and Australia. He has worked on marine engineering, drinking water, and sewage treatment projects. Throughout his career, analysing data has been a central theme. He also has a PhD in marketing and is the author of Customer Experience Management for Water Utilities. In his work, he aims to combine the social sciences with engineering to create value for customers. Peter occasionally lectures marketing for MBA students. He is currently responsible for developing and implementing the data science strategy for a water utility in regional Australia. The objective of this strategy is to create value from data through useful, sound, and aesthetic data science. His mission is to breed unicorn data scientists by motivating other water professionals to ditch their spreadsheets and learn how to write code.
Read more about Peter Prevos

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