scholarly journals Data Science Technologies in Economics and Finance: A Gentle Walk-In

2021 ◽  
pp. 1-17
Author(s):  
Luca Barbaglia ◽  
Sergio Consoli ◽  
Sebastiano Manzan ◽  
Diego Reforgiato Recupero ◽  
Michaela Saisana ◽  
...  
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Economic Activity ◽  
Data Science ◽  
Economic Modeling ◽  
Data Handling ◽  
Use Of Data ◽  
The Subject ◽  
The Common

AbstractThis chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance.

scholarly journals Characterizing Big Data Management

10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals Social Network Extraction Unsupervised

2021 ◽  
Vol 12 (3) ◽  
pp. 4443-4449
Author(s):  
Mahyuddin K. M. Nasution Et.al
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Information Technology ◽  
Big Data ◽  
Social Network ◽  
Data Science ◽  
Information Sources ◽  
Scientific Data

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

The Ethics of the Ethics of AI

2020 ◽  
pp. 25-51
Author(s):  
Thomas M. Powers ◽  
Jean-Gabriel Ganascia
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Science ◽  
Scientific Explanation ◽  
Applied Ethics ◽  
Machine Ethics ◽  
Ethical Problems ◽  
Computational Data ◽  
The Common ◽  
Novel Approaches

This chapter discusses several challenges for doing the ethics of artificial intelligence (AI). The challenges fall into five major categories: conceptual ambiguities within philosophy and AI scholarship; the estimation of AI risks; implementing machine ethics; epistemic issues of scientific explanation and prediction in what can be called computational data science (CDS), which includes “big data” science; and oppositional versus systemic ethics approaches. The chapter then argues that these ethical problems are not likely to yield to the “common approaches” of applied ethics. Primarily due to the transformational nature of artificial intelligence within science, engineering, and human culture, novel approaches will be needed to address the ethics of AI in the future. Moreover, serious barriers to the formalization of ethics will be needed to overcome to implement ethics in AI.

Exploring the Usage of Big Data Analytical Tools in Telecommunication Industry in Oman

Web Services ◽  
2019 ◽  
pp. 459-472
Author(s):  
Himyar Ali Al Jabri ◽  
Ali H. Al-Badi ◽  
Oualid Ali
Keyword(s):  
Information Technology ◽  
Social Media ◽  
Big Data ◽  
Data Management ◽  
Competitive Advantage ◽  
Case Studies ◽  
Telecommunication Industry ◽  
A Value ◽  
Telecom Industry ◽  
Analytical Tools

Big Data has recently become a very hot topic in the field of Information Technology and Data Management. Data generated by the company's daily operations through different resources such as social media, etc. is very important because it can bring a value that will lead to a competitive advantage. The objectives of this research are to: 1) Explore the analytical tools used to manipulate Big Data in Omani telecom industry, 2) Present the benefits of using these tools, the extent of use, and the features specifically promoted these tools, and 3) Highlight the challenges/obstacles that the telecom industry in Oman facing in adopting/using Big Data analytical tools. To achieve the research objectives two case studies were conducted among the main telecom operators in Oman. This research concluded that both studied telecom operators in Oman are not ready for the DBAs. Both operators need to invest in developing the capabilities that enable them to use these tools. Once that is satisfied, then other components like the infrastructure, tools, and data can be managed very well.

Digital Epidemiology of Innovation (Preprint)

2021 ◽  
Author(s):  
Ivan Triana ◽  
LUIS PINO ◽  
Dennise Rubio
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Management ◽  
Medical Practice ◽  
Data Science ◽  
Medical Literature ◽  
The Future ◽  
Digital Epidemiology ◽  
Definition Of ◽  
And Control

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

scholarly journals Cyclical fluctuations in freight transport in selected european union countries. Synchronization, or not?

2018 ◽  
Vol 80 ◽  
pp. 49-58
Author(s):  
Sławomir Dorosiewicz
Keyword(s):  
European Union ◽  
Economic Activity ◽  
The European Union ◽  
Demand And Supply ◽  
Macroeconomic Variables ◽  
Business Fluctuations ◽  
Cyclical Fluctuations ◽  
External Context ◽  
The Subject ◽  
The Common

Fluctuations of the economic activity in transport result from the operation of many factors of a demand and supply nature in all sectors of the economy. These factors determine both the common and specific characteristics of such fluctuations. The aim of this paper is not only to examine the morphological features of cyclical fluctuations on the transport market in Poland and selected countries of the European Union, but also the degree of their synchronization. The latter can be understood in the external context (between fluctuations in the transport production of various countries), but also in the internal one, where the subject of comparisons are fluctuations in transport and basic macroeconomic variables. The properties of business fluctuations will be examined by classical procedures for the separation of cyclical components and the detection of their turning points.

Data Science and Big Data Practice Using Apache Spark and Python

2021 ◽  
pp. 67-95
Author(s):  
Li Chen ◽  
Lala Aicha Coulibaly
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Computer Science ◽  
Data Analytics ◽  
Data Science ◽  
Principal Component ◽  
Real Data ◽  
Apache Spark ◽  
Data Sets ◽  
Information Technology Students

Data science and big data analytics are still at the center of computer science and information technology. Students and researchers not in computer science often found difficulties in real data analytics using programming languages such as Python and Scala, especially when they attempt to use Apache-Spark in cloud computing environments-Spark Scala and PySpark. At the same time, students in information technology could find it difficult to deal with the mathematical background of data science algorithms. To overcome these difficulties, this chapter will provide a practical guideline to different users in this area. The authors cover the main algorithms for data science and machine learning including principal component analysis (PCA), support vector machine (SVM), k-means, k-nearest neighbors (kNN), regression, neural networks, and decision trees. A brief description of these algorithms will be explained, and the related code will be selected to fit simple data sets and real data sets. Some visualization methods including 2D and 3D displays will be also presented in this chapter.

scholarly journals Big data analytics - towards the enrichment of content tourism for revitalization of Japanese rural area

2018 ◽  
Vol 169 ◽  
pp. 01008 ◽  
Author(s):  
Ali Bakdur ◽  
Fumito Masui ◽  
Michal Ptaszynski
Keyword(s):  
Big Data ◽  
Rural Areas ◽  
Data Analytics ◽  
Data Science ◽  
Public Awareness ◽  
Big Data Analytics ◽  
Tourism Industry ◽  
Analysis Model ◽  
Travel And Tourism ◽  
Use Of Data

Japan's domestic travel and tourism industry expenditure has been declining gradually since 1998 (from 33.5 in 1998 to 21.6 trillion JPY in 2016). Our research purpose is to construct a data analysis model to transform the collected data to a meaningful graphical format by using big data analytics techniques to discover anomalies and sustainable development possibilities for economy and tourism of Japan's rural areas, with a particular focus on the prefecture of Hokkaido, subprefecture of Okhotsk. To strengthen the reliability of this model we apply popular Monte Carlo simulation combined with Bayesian statistic and implement it on an Apache Spark platform to acquire results within the span of the study. Through this research, we focus on observing and analyzing interests, expectations and tendencies of Japanese people living in rural areas. From such collected information, we can obtain reasons for the decline of this sector’s impact on Japan’s economy. Measuring public awareness has become more efficient since the content generator role has been passed on to ordinary people. Therefore, the analysis of Big Data with the use of data science techniques has become important to comprehend human behavior from multiple points of view, including the scientific, economic, political, historical and sociological.

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