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In recent years, intense usage of computing has been the main strategy of investigations in several scientific research projects. The progress in computing technology has opened unprecedented opportunities for systematic collection of experimental data and the associated analysis that were considered impossible only few years ago.

This paper focuses on the strategies in use: it reviews the various components that are necessary for an effective solution that ensures the storage, the long term preservation, and the worldwide distribution of large quantities of data that are necessary in a large scientific research project.

The paper also mentions several examples of data management solutions used in High Energy Physics for the CERN Large Hadron Collider (LHC) experiments in Geneva, Switzerland which generate more than 30,000 terabytes of data every year that need to be preserved, analyzed, and made available to a community of several tenth of thousands scientists worldwide.

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.

In recent years, with the rapid development and application of the Internet, big data, and information science in recent years, there has been an increase in the number of types of public information about the financial market. And stock market investors are gradually obtaining relevant information through network platforms and mobile terminals to assist their investment decisions. GEM-listed companies are primarily high-tech enterprises with relatively prominent main businesses, unique technology, and significant product market potential, and they are attracting increasing investor interest. Compared with the large enterprises on the main board, the high-tech enterprises in the GEM have serious problems of “high stock price, high price-earnings ratio, and high fundraising”, but the “high growth” is not prominent enough. Based on behavioral finance theory, combined with professional knowledge and analytical frameworks such as econometrics, statistics, and computer science, and using big data, this paper conducts an empirical analysis and research on the factors affecting the value of GEM companies. The results show that the profitability, solvency, operating ability, growth ability, enterprise scale, and executive characteristics of enterprises all have a significant impact on the value of listed companies. As a result, listed companies should be based on their positioning, make full use of modern information means such as Internet big data, and continuously improve the enterprise’s profitability, solvency, operating ability, and growth ability, as well as enhance the qualifications, ability, and personal charm of leaders, to increase enterprise value. At the same time, the GEM companies should take advantage of the development opportunities of the “Belt and Road” to solve the problem of unreasonable regional distribution, so that the GEM companies will develop faster and better.