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.

The Internet according to kids

2002 ◽  
Vol 3 (2) ◽  
pp. 45-52
Author(s):  
Jorian Clarke
Keyword(s):  
Parental Involvement ◽  
Mobile Phones ◽  
Online Shopping ◽  
Online Banking ◽  
Internet Usage ◽  
The Internet ◽  
The Future ◽  
Child Friendly ◽  
Over Time

Describes a six‐year study of children’s Internet usage which shows how preferences and habits have changed over time; this was conducted by SpectraCom Inc and Circle 1 network. Explains the research methodology and the objectives, which were to identify trends in the amount of time spent by children online now and in future, their opinions about the future role of the Internet in society and the future of e‐commerce, and parents’ roles in children’s online activities. Concludes that there is need for a more child‐friendly content in Internet sites and for more parental involvement, that children will be influential in the market for alternative devices like mobile phones, that online shopping is likely to flourish, and that children have a growing interest in online banking.

scholarly journals Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

2018 ◽  
Author(s):  
Jen Schradie
Keyword(s):  
Big Data ◽  
Data Science ◽  
Digital Data ◽  
The Internet ◽  
Sociological Research ◽  
Marginalized Populations ◽  
Online Data ◽  
Persistent Problem ◽  
Current State ◽  
Using Data

With a growing interest in data science and online analytics, researchers are increasingly using data derived from the Internet. Whether for qualitative or quantitative analysis, online data, including “Big Data,” can often exclude marginalized populations, especially those from the poor and working class, as the digital divide remains a persistent problem. This methodological commentary on the current state of digital data and methods disentangles the hype from the reality of digitally produced data for sociological research. In the process, it offers strategies to address the weaknesses of data that is derived from the Internet in order to represent marginalized populations.

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 in systemic sclerosis: Great potential for the future

2020 ◽  
Vol 5 (3) ◽  
pp. 172-177
Author(s):  
Mislav Radic ◽  
Tracy M Frech
Keyword(s):  
Big Data ◽  
Systemic Sclerosis ◽  
Literature Review ◽  
Rare Diseases ◽  
Unstructured Data ◽  
Full Potential ◽  
Traditional Methods ◽  
Processing Data ◽  
The Future ◽  
Low Prevalence

Since it was first used in 1997, the term “big data” has been popularized; however, the concept of big data is relatively new to medicine. Big data refers to a method and technique to systematically retrieve, collect, manage, and analyze very large and complex sets of structured and unstructured data that cannot be sufficiently processed using traditional methods of processing data. Integrating big data in rare diseases with low prevalence and incidence, like systemic sclerosis is of particular importance. We conducted a literature review of use of big data in systemic sclerosis. The volume of data on systemic sclerosis has grown steadily in the recent years; however, big data methods have not been readily used. This inexhaustible source of data needs to be used more to unleash its full potential.

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.

Social Impacts of Cyber Culture and Predictions About the Future of Open and Distance Education

2021 ◽  
pp. 135-153
Author(s):  
Buket Kip Kayabaş
Keyword(s):  
Distance Education ◽  
Information And Communication Technologies ◽  
Communication Technologies ◽  
The Internet ◽  
Daily Lives ◽  
Cyber Culture ◽  
The Future ◽  
Information And Communication ◽  
Open And Distance Learning ◽  
Over Time

Developments in information and communication technologies play a major role in shaping economic, political, and cultural fields. Together with its inherent features, the internet, in addition to offering opportunities such as a new cultural space, freedom, and reality, has led the change of learning habits, cultural forms, and identities. Open and distance learning starting from correspondence education to computer networks-based education is one of the most affected areas by internet technologies. Various applications have developed in the field of open and distance education over time with the reflections of cyber culture. The aim of this study is to define cyber culture with its components and examine which areas it affects in our daily lives then to investigate the future open and distance education applications shaped by cyber culture.

scholarly journals An Intellectual History of the Concepts of Premier-Presidentialism and President-Parliamentarism

2019 ◽  
Vol 18 (1) ◽  
pp. 12-29 ◽  
Author(s):  
Robert Elgie
Keyword(s):  
Intellectual History ◽  
Meta Analysis ◽  
Academic Work ◽  
Early History ◽  
Future Application ◽  
Contemporary History ◽  
Journal Articles ◽  
The Future ◽  
History Of ◽  
Over Time

Shugart and Carey introduced the twin concepts of premier-presidentialism and president-parliamentarism in their 1992 volume, Presidents and Assemblies. Based on a meta-analysis of journal articles and book publications, this article distinguishes between an early and a contemporary history of the two concepts. The period of early history runs from 1992 to around 2009. This was the time when the two concepts were entering the academic consciousness and when there was also some typological and classificatory ambiguity. The period of contemporary history begins in 2010. This era is marked by conceptual and classificatory clarity and by an increasing reference to the two concepts in academic work. In the article, we show how the concepts have been applied over time, noting a number of changes across the two periods under consideration. We conclude by pointing out some challenges to the future application of the two concepts.

Integrating Systems Modelling and Data Science

2016 ◽  
Vol 5 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Erik Pruyt
Keyword(s):  
Big Data ◽  
System Dynamics ◽  
Data Science ◽  
Modelling And Simulation ◽  
System Dynamics Modelling ◽  
The Future ◽  
Systems Modelling ◽  
Dynamics Modelling

Although System Dynamics modelling is sometimes referred to as data-poor modelling, it often is –or could be– applied in a data-rich manner. However, more can be done in the era of ‘big data'. Big data refers here to situations with much more available data than was until recently manageable. The field of data science makes big(ger) data manageable. This paper provides a perspective on the future of System Dynamics with a prominent place for bigger data and data science. It discusses different approaches for dealing with bigger data. It reviews methods, techniques and tools for dealing with bigger data in System Dynamics, and sheds light on the modelling phases for which data science is most useful. Finally, it provides several examples of current applications in which big data, data science, and System Dynamics modelling and simulation are being merged.

scholarly journals The role of data science in healthcare advancements: applications, benefits, and future prospects

2021 ◽  
Author(s):  
Sri Venkat Gunturi Subrahmanya ◽  
Dasharathraj K. Shetty ◽  
Vathsala Patil ◽  
B. M. Zeeshan Hameed ◽  
Rahul Paul ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Healthcare Applications ◽  
Interdisciplinary Field

AbstractData science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

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