scholarly journals How Computational Statistics Became the Backbone of Modern Data Science

2011 ◽  
pp. 3-16
Author(s):  
James E. Gentle ◽  
Wolfgang Karl Härdle ◽  
Yuichi Mori
Keyword(s):  
Data Science ◽  
Computational Statistics

Computational Statistics and Data Science in the Twenty‐First Century

2021 ◽  
pp. 1-17
Author(s):  
Andrew J. Holbrook ◽  
Akihiko Nishimura ◽  
Xiang Ji ◽  
Marc A. Suchard
Keyword(s):  
Data Science ◽  
First Century ◽  
Computational Statistics ◽  
Twenty First Century

Computational Statistics of Data Science for Secured Software Engineering

2021 ◽  
pp. 81-96
Author(s):  
Raghavendra Rao Althar ◽  
Debabrata Samanta
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Data Science ◽  
Systems Development ◽  
Data Sources ◽  
Software Systems ◽  
Computational Statistics ◽  
Critical Dimensions ◽  
Work Done

The chapter focuses on exploring the work done for applying data science for software engineering, focusing on secured software systems development. With requirements management being the first stage of the life cycle, all the approaches that can help security mindset right at the beginning are explored. By exploring the work done in this area, various key themes of security and its data sources are explored, which will mark the setup of base for advanced exploration of the better approaches to make software systems mature. Based on the assessments of some of the work done in this area, possible prospects are explored. This exploration also helps to emphasize the key challenges that are causing trouble for the software development community. The work also explores the possible collaboration across machine learning, deep learning, and natural language processing approaches. The work helps to throw light on critical dimensions of software development where security plays a key role.

Data science in organizations: Conceptualizing its breakthroughs and blind spots

2021 ◽  
pp. 026839622098853
Author(s):  
Jacob L Cybulski ◽  
Rens Scheepers
Keyword(s):  
Data Science ◽  
Theoretical Principle ◽  
Science Literature ◽  
Computational Statistics ◽  
Data Intensive ◽  
Requisite Variety ◽  
Cross Domain ◽  
Law Of Requisite Variety ◽  
Complex Phenomena ◽  
Blind Spots

The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.

How We Became Instrumentalists (Again)

2018 ◽  
Vol 48 (5) ◽  
pp. 673-684 ◽  
Author(s):  
Matthew L. Jones
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Data Science ◽  
Predictive Analytics ◽  
The United States ◽  
Applied Statistics ◽  
Computational Statistics ◽  
Special Issue ◽  
Epistemic Values

In the last two decades, a highly instrumentalist form of statistical and machine learning has achieved an extraordinary success as the computational heart of the phenomenon glossed as “predictive analytics,” “data mining,” or “data science.” This instrumentalist culture of prediction emerged from subfields within applied statistics, artificial intelligence, and database management. This essay looks at representative developments within computational statistics and pattern recognition from the 1950s onward, in the United States and beyond, central to the explosion of algorithms, techniques, and epistemic values that ultimately came together in the data sciences of today. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

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