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You're reading from  Principles of Strategic Data Science

Product typeBook
Published inJun 2019
Reading LevelExpert
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ISBN-139781838985295
Edition1st Edition
Languages
Python
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Data Science
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Peter Prevos
Peter Prevos
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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.
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Process

Chapter 2, Good Data Science, mentioned the requirement for governance in data science to ensure the outcomes of projects are sound. The process of creating value from data follows an iterative workflow that works from raw data to a finished project. (Wickham, H., & Grolemund, G. (2016). R for data science: Import, Tidy, Transform, Visualize, and Model Data Sebastopol, CA: O'Reilly. Available at—https://r4ds.had.co.nz/). The workflow starts with defining a problem that needs solving as shown in Figure 4.2. The next step involves loading and transforming the data into a format that is suitable for the required analysis. The data science workflow contains a loop that consists of exploration, modelling, and reflection, which is repeated until the problem is solved or is shown to be unsolvable.

Figure 4.2: Data science workflow
Figure 4.2: Data science workflow

The workflow for a data project is independent of the aspect of the data science continuum under consideration...

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Principles of Strategic Data Science
Published in: Jun 2019Publisher: ISBN-13: 9781838985295
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Author (1)

author image
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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