Data Science /Наука о данных Год издания: 2018 Автор: John D. Kelleher, Brendan Tierney /Джон Келлехер, Брендан Тирни Жанр или тематика: Наука о данных Издательство: The MIT Press ISBN: 978-0-262-53543-4 Серия: MIT Press Essential Knowledge Язык: Английский Формат: PDF Качество: Издательский макет или текст (eBook) Интерактивное оглавление: Да Количество страниц: 282 - Описание Основной задачей науки о данных является повышение качества принятия решений путем анализа данных. Сегодня наука о данных определяет содержимое рекламы, которую мы видим в интернете, названия рекомендуемых нам книг и фильмов, помогает фильтровать спам в сообщениях электронной почты и даже устанавливает сумму нашей медицинской страховки. Данная книга из серии MIT Press Essential Knowledge представляет собой краткое введение в развивающуюся науку о данных, рассказывает об ее эволюции и современном применении, о связи с машинным обучением, вопросах инфраструктуры данных и проблемах этического характера. Overview The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. Authors John D. Kelleher is a Professor of Computer Science and the Academic Leader of the Information, Communication, and Entertainment Research Institute at the Dublin Institute of Technology. His research is supported by the ADAPT Centre, which is funded by Science Foundation Ireland (Grant 13/RC/2106) and co-funded by the European Regional Development fund. He is the coauthor of Fundamentals of Machine Learning for Predictive Data Analytics (MIT Press). Brendan Tierney is a lecturer in the School of Computing at the Dublin Institute of Technology, an Oracle ACE director, and the author of a number of books on data mining using Oracle technology.
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