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Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

机译：数据科学：大数据科学和分析时代的新范式

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摘要

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/ 08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science's terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.
机译：作为一个新兴的查询领域，数据科学服务于信息技术世界和应用科学。数据科学是一个众所周知的术语，倾向于与术语大数据同义。但是，数据科学是通过数学和计算研究发现的解决方案的应用，而大数据科学则描述了有关数据分析的问题，涉及体积，变化和速度（3V）。尽管从科学的角度来看，数据科学在理论上没有太多发展，但仍有巨大的增长机会。事实证明，由于越来越需要了解正在产生的不可克服的数据量以及需要进行分析，因此数据科学对IT行业至关重要。简而言之，数据无处不在，格式各异。科学家目前正在使用诸如机器学习方法之类的统计和AI分析技术来理解海量数据集，并且自然而然地，他们试图寻找数据集之间的关系。在过去的十年中，使用诸如Hadoop和Apache Spark之类的工具在云计算范式内开发软件系统，已帮助在数据科学领域取得了巨大进步[Z. Sun，L. Sun和K. Strang，大数据分析服务以增强商业智能，计算机信息系统杂志（2016），doi：10.1080 / 08874417.2016.1220239]。这些进步使科学家和IT专业人员都可以使用云计算基础架构来每天处理PB级数据。对于沃尔玛，英伟达和谷歌等大型私营公司而言尤其如此。本文旨在探讨务实的方法，以研究在现代世界中如何实践数据科学（相对于大数据科学）。我们还将研究数学和计算机科学如何帮助塑造大数据科学的领域。我们将重点介绍数学和计算机科学如何对数据科学方法，工具的发展产生重大影响，以及这些方法如何提出新的问题，以推动这些核心学科中涉及数据分析，机器学习和可视化的新研究领域。

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