scholarly journals Visualização de dados: passado, presente e futuro | Data vizualization: past, present and future

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Fabiano Couto Corrêa da Silva
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Science ◽  
Multivariate Data ◽  
Scientific Data ◽  
Cutting Edge ◽  
Cognitive Analysis ◽  
Analytical Thinking ◽  
Over Time

RESUMO São expostos os princípios fundamentais da ciência de dados e as generalidades de uma de suas áreas de estudo: a Visualização de dados. O artigo aborda como os dados multivariados tem sido representados por meio de imagens e gráficos ilustrados que relacionam os elementos de sintaxe e semântica que podem contemplar o pensamento analítico nas margens visuais. Analisa como a Visualização de Dados foi desenvolvida ao longo do tempo, utilizando exemplos reconhecidos como de vanguarda neste campo, validando a pesquisa com análise cognitivas básicas em princípios de apresentação de evidências nos displays de informação.Palavras-chave: Visualização de Dados; Infografias; Dados Científicos; Storytelling, Big Data.ABSTRACT The fundamental principles of data science and the generalities of one of its areas of study are exposed: Data Visualization. The article discusses how multivariate data has been represented through illustrated images and graphs that relate the elements of syntax and semantics that can include analytical thinking in visual margins. It analyzes how Data Visualization has been developed over time, using examples recognized as cutting edge in this field, validating research with basic cognitive analysis on principles of evidence presentation in information displays.Keywords: Data Visualization; Infographics; Scientific Data; Storytelling, Big Data.

Big Data, Data Science, and Career Pathways

2020 ◽  
pp. 239-254
Author(s):  
David W. Dorsey
Keyword(s):  
Big Data ◽  
Data Science ◽  
Career Pathways ◽  
Unstructured Data ◽  
Future Application ◽  
The Internet ◽  
The Future ◽  
Skill Requirements ◽  
Enormous Number ◽  
Over Time

With the rise of the internet and the related explosion in the amount of data that are available, the field of data science has expanded rapidly, and analytic techniques designed for use in “big data” contexts have become popular. These include techniques for analyzing both structured and unstructured data. This chapter explores the application of these techniques to the development and evaluation of career pathways. For example, data scientists can analyze online job listings and resumes to examine changes in skill requirements and careers over time and to examine job progressions across an enormous number of people. Similarly, analysts can evaluate whether information on career pathways accurately captures realistic job progressions. Within organizations, the increasing amount of data make it possible to pinpoint the specific skills, behaviors, and attributes that maximize performance in specific roles. The chapter concludes with ideas for the future application of big data to career pathways.

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.

Applying Big Data visualization to detect trends in 30 years of performance reports

Evaluation ◽  
2020 ◽  
Vol 26 (4) ◽  
pp. 516-540
Author(s):  
Eran Raveh ◽  
Yuval Ofek ◽  
Ron Bekkerman ◽  
Hertzel Cohen
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Science ◽  
Keyword Search ◽  
Mining Machine ◽  
Textual Information ◽  
Big Data Visualization ◽  
Performance Reports ◽  
User Friendly ◽  
Support Evaluation

Evaluators worldwide are dealing with a growing amount of unstructured electronic data, predominantly in textual format. Currently, evaluators analyze textual Big Data primarily using traditional content analysis methods based on keyword search, a practice that is limited to iterating over predefined concepts. But what if evaluators cannot define the necessary keywords for their analysis? Often we should examine trends in the way certain organizations have been operating, while our raw data are gigabytes of documents generated by that organization over decades. The problem is that in many cases we do not know what exactly we need to look for. In such cases, traditional analytical machinery would not provide an adequate solution within reasonable time—instead, heavy-lifting Big Data Science should be applied. We propose an automated, quantitative, user-friendly methodology based on text mining, machine learning, and data visualization, which assists researchers and evaluation practitioners to reveal trends, trajectories, and interrelations between bits and pieces of textual information in order to support evaluation. Our system automatically extracts a large amount of descriptive terminology for a particular domain in a given language, finds semantic connections between documents based on the extracted terminology, visualizes the entire document repository as a graph of semantic connections, and leads the user to the areas on that graph where most interesting trends can be observed. This article exemplifies the new method on 1700 performance reports, showing that the method can be used successfully, supplying evaluators with highly important information which cannot be revealed using other methods. Such exploratory exercise is vital as a preliminary exploratory phase for evaluations involving unstructured Big Data, after which a range of evaluation methods can be applied. We argue that our system can be successfully implemented on any domain evaluated.

Comparative Analysis of Data Visualization Libraries Matplotlib and Seaborn in Python

2021 ◽  
Vol 10 (1) ◽  
pp. 277-281
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Programming Languages ◽  
Data Visualization ◽  
Data Science ◽  
Computational Modelling ◽  
And Mathematics ◽  
Python Programming ◽  
The Comparative Study

With the tremendous growth in the areas of computing, statistics, and mathematics has led to the rise of the emerging field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named ‘Matplotlib’ and ‘Seaborn’. The sole purpose of this paper is to identify areas and evaluate the strengths and weaknesses of these libraries with the implementation of code and identify the classification of the univariate and multivariate plotting of data concerned with patterns of data visualization and computational modelling of data in the form of processed information using techniques of big data and data mining

Data Science is Here

2020 ◽  
pp. 108-127
Author(s):  
Dimitar Grozdanov Christozov ◽  
Katia Rasheva-Yordanova ◽  
Stefka Toleva-Stoimenova
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Science ◽  
Informed Decision Making ◽  
Knowledge And Skills ◽  
Analytical Skills ◽  
Analytical Thinking ◽  
Data Scientist ◽  
Primary Focus ◽  
Science Training

With the advent of big data, the search for respective data experts has become more intensive. This study aims to discuss data scientist skills and some topical issues that are related to data specialist profiles. A complex competence model has been deployed, dividing the skills into three groups: hard, soft, and analytical skills. The primary focus is on analytical thinking as one of the key competences of the successful data scientist taking into account the trans-discipline nature of data science. The chapter considers a new digital divide between the society and this small group of people that make sense out of the vast data and help the organization in informed decision making. As data science training needs to be business-oriented, the curricula of the Master's degree in Data Science is compared with the required knowledge and skills for recruitment.

scholarly journals The dynamics of big data and human rights: the case of scientific research

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160129 ◽  
Author(s):  
Effy Vayena ◽  
John Tasioulas
Keyword(s):  
Human Rights ◽  
Big Data ◽  
Data Science ◽  
Scientific Research ◽  
Dynamic Interaction ◽  
Digital Environment ◽  
Big Data Applications ◽  
Health Related Research ◽  
Health Related ◽  
Over Time

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities—the new capabilities and risks—of the rapidly evolving digital environment. This article is part of the themed issue ‘The ethical impact of data science’.

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.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

Produção internacional sobre ciência orientada a dados: análise dos termos data science e e-science na scopus e na web of science

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Leilah Santiago Bufrem ◽  
Fábio Mascarenhas Silva ◽  
Natanael Vitor Sobral ◽  
Anna Elizabeth Galvão Coutinho Correia
Keyword(s):  
Big Data ◽  
Open Access ◽  
Grid Computing ◽  
Digital Library ◽  
Data Science ◽  
Web Of Science ◽  
Computer Systems ◽  
Distributed Computer Systems

Introdução: A atual configuração da dinâmica relativa à produção e àcomunicação científicas revela o protagonismo da Ciência Orientada a Dados,em concepção abrangente, representada principalmente por termos como “e-Science” e “Data Science”. Objetivos: Apresentar a produção científica mundial relativa à Ciência Orientada a Dados a partir dos termos “e-Science” e “Data Science” na Scopus e na Web of Science, entre 2006 e 2016. Metodologia: A pesquisa está estruturada em cinco etapas: a) busca de informações nas bases Scopus e Web of Science; b) obtenção dos registros; bibliométricos; c) complementação das palavras-chave; d) correção e cruzamento dos dados; e) representação analítica dos dados. Resultados: Os termos de maior destaque na produção científica analisada foram Distributed computer systems (2006), Grid computing (2007 a 2013) e Big data (2014 a 2016). Na área de Biblioteconomia e Ciência de Informação, a ênfase é dada aos temas: Digital library e Open access, evidenciando a centralidade do campo nas discussões sobre dispositivos para dar acesso à informação científica em meio digital. Conclusões: Sob um olhar diacrônico, constata-se uma visível mudança de foco das temáticas voltadas às operações de compartilhamento de dados para a perspectiva analítica de busca de padrões em grandes volumes de dados.Palavras-chave: Data Science. E-Science. Ciência orientada a dados. Produção científica.Link:http://www.uel.br/revistas/uel/index.php/informacao/article/view/26543/20114

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