03571cam a2200445 i 4500 115199 115199 BJBSDDR 20230411090720.0 ta 171003s2018 maua b 001 0 eng 9780262535434 (pbk. : alk. paper) 0262535432 (pbk. : alk. paper) DLC eng DLC eng pcc QA 76.9 K29d 2018 005.7 Kelleher, John D., 1974- Data science / John D. Kelleher and Brendan Tierney. Cambridge, Massachusetts : The MIT Press, [2018] xi, 264 pages : illustrations ; 18 cm. text txt rdacontent unmediated n rdamedia volume nc rdacarrier The MIT Press essential knowledge series Includes bibliographical references and index. What is data science? -- What are data, and what is a dataset? -- A data science ecosystem -- Machine learning 101 -- Standard data science tasks -- Privacy and ethics -- Future trends and principles of success A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. 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 Provided by publisher Big data 1639 Aprendizaje automático (Inteligencia artificial) 16252 Procesamiento de datos 10794 Investigación cuantitativa 10495 Machine learning. Data mining. Quantitative research. Tierney, Brendan, 1970- 16253 8927 The MIT Press essential knowledge series 7 cbc orignew 1 ecip 20 y-gencatlg lcc BK 0 0 0 0 HU BJB BJB HU 2021-05-04 0 QA 76.9 K29d 2018 00000149965 2021-05-04 00:00:00 1 2021-05-04 BK