Data Science in Context: Foundations, Challenges, Opportunities Hardcover – Big Book, 20 October 2022

If you are a technology professional leading a software product development team big or small -- this book is for you. AI Mirage causes anxiety in your organization. You are expected to deliver AI-first products with broad near human capabilities, while you worry they may fail miserably at the simplest tasks even a child can reliably do.This book covers a few common challenges where software achieved or exceeded human-like capabilities. It shows their evolution from basic data science, through machine learning & AI into revolutionary consumer products. It highlights business, organizational, and societal challenges that necessarily arise when one is successful at scale. And most importantly -- it's written in plain business language, so you don't need to learn Python or upgrade your linear algebra skills.Few years back I worked directly for Alfred and Peter, who built the foundations of Google Research. Their pivotal ACM article with intuitions on how scientific methods can be used in complex organizations like Google have proven tremendously effective for many years. Similarly, I am confident that this very book offers the wisdom essential for your enterprise to be successful in the AI wars of the next decade.

In Data Science in Context, the authors present a broad overview of Data Science. Their introduction to the field includes defintions, a new evaluatory framework, and thorough investigations of specific examples with respect to that framework. More specifically, the framework is called the "Analysis Rubric" and provides a comprehensive approach for evaluating any application of Data Science. This data-scientific method involves seven key steps: the data, the algorithm, their dependability, their understandability, clear objectives, the implications of false positives or negatives, and ethical implications. Dependability encompasses the security of both the data and the algorithm from adversarial missuse. Understandability encompasses the ability to reproduce results and the ability to explain why a specific result is reached. The authors discuss the importance of a shared language, and Data Science in Context provides a common framework and shared language in the Analysis Rubric. Data Science in Context presents a comprehensive introduction to Data Science and demonstrates how valuable an understanding of Data Science is in our modern world, given the abundance of data and the rapid progression of computational power.