Abstract
Having understood the data science process and what it entails, we now delve into the business-fitment considerations of data science.
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Notes
- 1.
Supervisory control and data acquisition (SCADA) is widely adopted by industries for monitoring and controlling devices.
- 2.
Part 4 of this book covers the skills, roles, and typical structure in a data science team.
- 3.
Refer to Karamshuk (2013) for a detailed example, where mobility factors were based on Foursquare check-in data.
- 4.
For an example model, refer to Jeremy Curuksu, “Developing a business strategy by combining machine learning with sensitivity analysis,” https://aws.amazon.com/blogs/machine-learning/developing-a-business-strategy-by-combining-machine-learning-with-sensitivity-analysis/, November 13, 2019.
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Also referred to as data wrangling, munging, and other colorful terms.
- 7.
We will look at the skills framework of a data science team in Chapter 21.
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Raina, V., Krishnamurthy, S. (2022). Data Science and Your Business. In: Building an Effective Data Science Practice. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7419-4_2
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DOI: https://doi.org/10.1007/978-1-4842-7419-4_2
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