A Collection of Case Studies
There are many declinations of data science projects: with or without labeled data; stopping at data wrangling or involving machine learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with real time or close to real time execution requirements and with acceptably slower performances; showing the results in shiny reports or hiding the nitty and gritty behind a neutral IT architecture; and - last but not least - with large budgets or no budget at all.
In the course of my professional life, I have seen many of the above projects and their data science nuances. So much experience - and the inevitably of related mistakes - should not be lost. Therefore the idea of this book: a collection of data science case studies from past projects.
This book includes project reviews from IoT, financial industry, customer intelligence, social media, cybersecurity, and more.
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