000			a
999			_c33372 _d33372
008			241106b xxu||||| |||| 00| 0 eng d
020			_a9780262535434
082			_a005.7 _bKEL
100			_aKelleher, John D.
245			_aData science
260			_bMIT Press, _c2018 _aCambridge :
300			_axi, 264 p. ; _bill., _c18 cm.
365			_b849.69 _c₹ _d01
504			_aIncludes bibliographical references and index.
520			_a"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."--Provided by publisher. 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.
650			_aBig Data
650			_aDatabase Management
650			_aData Mining
650			_aData Science
650			_aMachine learning
650			_aQuantitative research
650			_aAssociation-rule mining
650			_aBackpropagatio
650			_aCRISP-DM
650			_aData warehouse
650			_aDecision tree
650			_aMachine learning
650			_aML algorithms
650			_aMoneyball
650			_aNeural network
650			_aScatterplot
650			_aSupervised learning
650			_aTarget attribute
700			_aTierney, Brendan
942			_2ddc _cBK