Search Results

You are looking at 1 - 10 of 1,498 items :

  • "data science" x
Clear All
Time Complexity, Inferential Uncertainty, and Spacekime Analytics
Series: De Gruyter STEM

maximize the quality and value of surgical care in an increasingly complex system set the stage for data-driven discoveries and insights to further transform surgery through a data science approach. Data science, in general, refers to a “study of generalizable extraction of knowledge from data” [ 14 ]. Data science has become an integral part of several scientific disciplines, including clinical medicine. As surgical disciplines continue to improve the quality and value of care through technology and evidence, data science will likewise enable their evolution through new

Statistics, Journal of the American Statistical Association, Vol.71, pp. 791-799 Ayres I. (2008), Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart, New York: Random House Publishing Group. Blair, D. C. (2002). Knowledge management: hype, hope, or help?. Journal of the American Society for Information Science and Technology, 53(12), 1019-1028 Cameron, W. B. (1963). Informal sociology: A casual introduction to sociological thinking. New York: Random House. D. Cielen, D., Meysman, A. D. B.,Ali, M. (2016). Introducing Data Science-Big data, machine learning

Conference from 2010 to 2012. He delivered a keynote speech at the First Asia-Pacific iSchool Conference in 2014, ACM SAC 2015 Conference, ER2015 Conference, EDB 2016 Conference, and A-LIEP 2016 Conference. 1 Big Data, the Industrial Revolution 4.0, and Data Science Big data has been with us for a while. During the past decade, we have witnessed the exponential growth of data and rapid advances in computing technologies. With these new trends, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments

Abstract

Data Science looks at raw numbers and informational objects created by different disciplines. The Digital Society creates information and numbers from many scientific disciplines. The amassment of data though makes is hard to find structures and requires a skill full analysis of this massive raw material. The thoughts presented here on DS2 - Data Science & Digital Society analyze these challenges and offers ways to handle the questions arising in this evolving context. We propose three levels of analysis and lay out how one can react to the challenges that come about. Concrete examples concern Credit default swaps, Dynamic Topic modeling, Crypto currencies and above all the quantitative analysis of real data in a DS2 context.

734 © Carl Hanser Verlag, München Jahrg. 115 (2020) 10 INDUSTRIAL DATA SCIENCEINDUSTRIE 4.0 Industrial Data Science erfolgreich implementieren Interviewstudie zu Erfolgsfaktoren und Hemmnissen Die Potenziale von Industrial Data Science haben Unternehmen un- längst erkannt, scheitern jedoch an deren Umsetzung. In diesem Beitrag werden die Ergebnisse einer branchenübergreifenden Interviewstudie mit über 50 Führungskräften und Fachexperten vorgestellt, wobei Durchführungshemmnisse und Erfolgsfaktoren identifiziert werden. Zu- dem werden Anforderungen an das

, München 2018 10. acatech – Deutsche Akademie der Technikwissenschaften : Kompetenzentwicklungsstudie Industrie 4.0 – Erste Ergebnisse und Schlussfolgerungen . acatech , München 2016 acatech – Deutsche Akademie der Technikwissenschaften : Kompetenzentwicklungsstudie Industrie 4.0 – Erste Ergebnisse und Schlussfolgerungen . acatech , München 2016 11. Kennet , S. R. ; Zonnesheim , A. ; Fortuna , G. : A Road Map for Applied Data Science Supporting Sustainability in Advanced Manufacturing: The Information Quality Dimensions . In: Procedia Manufacturing 21–15 th

Series: De Gruyter STEM

Jane Greenberg is the Alice B. Kroeger Professor and Director of the Metadata Research Center http://cci.drexel.edu/mrc/ at the College of Computing and Informatics, Drexel University. Her research activities focus on metadata, knowledge organization/ semantics, linked data, data science, and economics. She serves on the advisory board of the Dublin Core Metadata Initiative (DCMI) and the steering committee for the NSF Northeast Big Data Innovation Hub (NEBDIH) http://nebigdatahub.org/ . She is a principal investigator on the NSF Spoke initiative, “A Licensing