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IEEE Computer Graphics and Applications, 2017 Data science methods and approaches address all stages of transition from data to knowledge and action. Visualization of this data is essential for human understanding of the subject under study, analytical reasoning about it, and generating new knowledge.
Gennady L. Andrienko +2 more
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The Role of Data Science in Web Science [PDF]
IEEE Intelligent Systems, 2016 Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data ...
Christopher Phethean +4 more
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Security of Data Science and Data Science for Security [PDF]
, 2019 In this chapter, we present a brief overview of important topics regarding the connection of data science and security. In the first part, we focus on the security of data science and discuss a selection of security aspects that data scientists should consider to make their services and products more secure.
Tellenbach, Bernhard +2 more
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Data science: connotation, methods, technologies, and development
Data Science and Management, 2021 The rapid development of big data breeds data science. Understanding and mastering the internal pattern of the value generation of big data is important for improving digitization and the covergence of data science with management science, computer ...
Zongben Xu +3 more
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Teaching Stats for Data Science [PDF]
, 2017 “Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records.
Kaplan, Daniel
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A Data Science Course for Undergraduates: Thinking with Data [PDF]
, 2015 Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly ...
Baumer, Ben
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Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice
Journal of Statistics and Data Science Education, 2022 This article presents the Data Science Ethos Lifecycle, a tool for engaging responsible workflow developed by an interdisciplinary team of social scientists and data scientists working with the Academic Data Science Alliance. The tool uses a data science
Margarita Boenig-Liptsin +2 more
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Prospecting (in) the data sciences [PDF]
Big Data & Society, 2020 Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science.
Stephen C. Slota +3 more
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Communications of the ACM, 2017 While it may not be possible to build a data brain identical to a human, data science can still aspire to imaginative machine thinking.
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