Dan Meador - Building Data Science Solutions with Anaconda: A comprehensive starter guide to building robust and complete models

A comprehensive starter guide to building robust and complete models

Dan Meador

BIRMINGHAMMUMBAI

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Data science has transformed not only the software industry but also the physical sciences, sociology, engineering, government, and society at large. While artificial intelligence has its roots in the earliest days of computer science, the rate of evolution and change in data science over the last decade is astounding. Yet even professionals in the field can struggle to keep up, and those who wish to join the profession can feel daunted by the amount they need to learn.

One of the keys to the pace of innovation in data science is the amount of energy expended on open source by researchers, universities, and companies alike. Making the most advanced tools of data science available to everyone lowers many of the barriers to entry for practitioners and encourages new and better tools.

We have been proud to be part of this open source data science revolution at Anaconda for the last 10 years. Over 25 million data scientists and other numerical computing professionals and students use Anaconda Distribution. Our relationship with this community gives us tremendous insights into the cutting-edge practices and the day-to-day work of people in the field.

Dan Meador gives you a valuable grounding in using open source data science tools to solve real-world problems in this book. Once you have completed your reading, you will understand many of the mechanisms, practices, and challenges underpinning modern data science. Dan's experience in the field and as the manager of Anaconda's conda command-line tool and its Navigator desktop app give him unique insight into the tools that millions use daily for their work.

Especially relevant to today's data practitioners is the second section, covering the topics of bias in AI/ML and choosing the best algorithms. When data science is (rightfully) challenged for the harm it can cause, it is not because of the malicious intent of data scientists but rather the lack of understanding of these critical issues. Therefore, being aware of bias in our algorithms and models is crucial for any data professional.

Whether you are interested in learning the tools of the trade for data science or an experienced professional looking to expand your knowledge, you will find this book to be a valuable resource and a foundation to explore these topics and others at another level of depth.

All the best on your journey!

Foreword by Kevin Goldsmith, CTO at Anaconda

Dan Meador is an engineering manager at Anaconda leading the conda team and championing open source. He also holds a patent for his work on AI systems and has grown his experience in AI/ML by creating AutoML solutions. He has seen how the power of data can work in everything from startups to Fortune 10 companies.

Andre Ye is a deep learning researcher at the University of Washington, focusing on improving the robustness and performance of deep computer vision systems for domain-specific applications. Documenting the field of data science is one of his strongest passions. He has written over 300 data science articles in various online publications and has published Modern Deep Learning Design and Application Development, a book exploring modern developments in designing effective deep learning systems. In his spare time, Andre enjoys keeping up with current data science research and jamming to hard metal.

Keith Moore is the chief product officer (CPO) for AutoScheduler.AI. He works with consumer goods, beverage, and distribution companies to drive efficiency in distribution centers. As the CPO, Moore's focus is on creating the future with the prescriptive warehouse. Moore was voted by Hart Energy Magazine as an Energy Innovator of the Year in 2020, was selected as a Pi Kappa Phi 30 under 30 member, and holds multiple patents in the fields of neural architecture search and supply chain planning. Moore attended the University of Tennessee, where he received a bachelor of science in mechanical engineering.