Data Science for Executives: Leveraging Machine Intelligence to Drive Business ROI by Nir Kaldero

Author:Nir Kaldero [Kaldero, Nir]
Language: eng
Format: azw3
Publisher: Lioncrest Publishing
Published: 2018-10-09T16:00:00+00:00

In Summary

The above description of the ten generic, high-level steps in the data-science workflow has been intended to expose you to a mindset: to show you what data scientists and technical teams do at each phase of this workflow journey and to demonstrate that the workflow involves technical and non-technical teams working together to seize opportunities and realize ROI.

Along the way, be prepared to learn through an iterative process of trial and error, just as machine-intelligence models do. There is no single winning horse, no model you can be sure is the right one before it is tested.

I have learned—thanks, Shachar—that “everything we do is wrong, but it’s always better to be wrong and strong than wrong and weak.” By leveraging data, you are strong even if you are wrong, that is, if you have picked the wrong model. If you only go on gut feeling, you’re being wrong and weak. Machine-intelligence models try to imitate life, but no one can predict the future with 100 percent accuracy. We live in a probabilistic and uncertain world. In learning from past data and trends, machine-intelligence models take account of and even imitate this uncertainty.

The point is that some models will yield greater accuracy than others when addressing specific business problems. The case studies in the third part of this book will give you techniques for determining which model best fits your business problem and the associated data.

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