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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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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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Risk Assessment for Scientific Data
Data Science Journal, 2020 Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use.
Matthew S. Mayernik +6 more
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Thoughts on Starting the CODATA Data Science Journal
Data Science Journal, 2023 This essay discusses some of the considerations that led to the founding of the [CODATA] Data Science Journal. Three factors were most relevant to the founding.
John Rumble
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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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Data Science and Prediction [PDF]
SSRN Electronic Journal, 2012 Big data promises automated actionable knowledge creation and predictive models for use by both humans and computers.
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Looking Back to the Future: A Glimpse at Twenty Years of Data Science
Data Science Journal, 2023 This paper carries out a lightweight review to explore the potentials of data science in the last two decades and especially focuses on the four essential components: data resources, technologies, data infrastructures, and data education. Considering the
Lili Zhang
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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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Data Science Journal, 2015 Currently, a huge amount of data is being rapidly generated in cyberspace. Datanature (all data in cyberspace) is forming due to a data explosion. Exploring the patterns and rules in datanature is necessary but difficult.
Yangyong Zhu, Yun Xiong
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