scholarly journals Various Algorithms & Techniques Driving Data Science for Big Data

2020 ◽  
Vol 9 (5) ◽  
pp. 725-727
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Science ◽  
Information Science ◽  
Big Business ◽  
Level Data

In basic terms, Big Data1 – when joined with Data Science2 – permit chiefs to gauge and survey fundamentally more data about the nuances of their organizations, and to utilize the data in settling on progressively keen choices. In early 2010, during the period when the development of Big Data was truly increasing noteworthy notification all through the 3Data Management industry, said that it "is advancing into the key reason for rivalry." It has now developed, information volumes proceed to develop, and now the inquiry is never again if it's another pattern and what influences it will have, yet how to use Big Data in significant manners for the venture. Information Science has been around for any longer than Big Data, yet it wasn't until the development of information volumes arrived at contemporary levels that Data Science has become an essential part of big business level Data Management.

scholarly journals A Report of Data-Intensive Capability, Institutional Support, and Data Management Practices in Social Sciences

2016 ◽  
Vol 11 (1) ◽  
pp. 156 ◽  
Author(s):  
Wei Jeng ◽  
Liz Lyon
Keyword(s):  
Social Sciences ◽  
Data Management ◽  
Data Science ◽  
Management Practices ◽  
Information Science ◽  
Institutional Support ◽  
Current Data ◽  
Management Support ◽  
Model Framework ◽  
Data Intensive

We report on a case study which examines the social science community’s capability and institutional support for data management. Fourteen researchers were invited for an in-depth qualitative survey between June 2014 and October 2015. We modify and adopt the Community Capability Model Framework (CCMF) profile tool to ask these scholars to self-assess their current data practices and whether their academic environment provides enough supportive infrastructure for data related activities. The exemplar disciplines in this report include anthropology, political sciences, and library and information science. Our findings deepen our understanding of social disciplines and identify capabilities that are well developed and those that are poorly developed. The participants reported that their institutions have made relatively slow progress on economic supports and data science training courses, but acknowledged that they are well informed and trained for participants’ privacy protection. The result confirms a prior observation from previous literature that social scientists are concerned with ethical perspectives but lack technical training and support. The results also demonstrate intra- and inter-disciplinary commonalities and differences in researcher perceptions of data-intensive capability, and highlight potential opportunities for the development and delivery of new and impactful research data management support services to social sciences researchers and faculty. 

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.

Fundamentals of deep data science

2020 ◽  
Vol 10 (2) ◽  
pp. 1-6
Author(s):  
Naveena M
Keyword(s):  
Big Data ◽  
Data Science ◽  
Information Science ◽  
Basic Information ◽  
Huge Impact

The present study reports the fundamentals of deep data sciences and their emerging roles across the globe. The influence of deep data science plays important roles in information science. The tools and softwares designed using big data science are creating huge impact on the society. Keeping these into consideration, the study reports the beneficial aspect of it along with basic information.

Changes in academic libraries in the era of Open Science

2020 ◽  
Vol 36 (3) ◽  
pp. 281-299
Author(s):  
Stefka Tzanova
Keyword(s):  
Big Data ◽  
Data Management ◽  
Academic Libraries ◽  
Data Science ◽  
Academic Library ◽  
Open Data ◽  
Open Science ◽  
Management Plan ◽  
Educational Process ◽  
Library Services

In this paper we study the changes in academic library services inspired by the Open Science movement and especially the changes prompted from Open Data as a founding part of Open Science. We argue that academic libraries face the even bigger challenges for accommodating and providing support for Open Big Data composed from existing raw data sets and new massive sets generated from data driven research. Ensuring the veracity of Open Big Data is a complex problem dominated by data science. For academic libraries, that challenge triggers not only the expansion of traditional library services, but also leads to adoption of a set of new roles and responsibilities. That includes, but is not limited to development of the supporting models for Research Data Management, providing Data Management Plan assistance, expanding the qualifications of library personnel toward data science literacy, integration of the library services into research and educational process by taking part in research grants and many others. We outline several approaches taken by some academic libraries and by libraries at the City University of New York (CUNY) to meet necessities imposed by doing research and education with Open Big Data – from changes in libraries’ administrative structure, changes in personnel qualifications and duties, leading the interdisciplinary advisory groups, to active collaboration in principal projects.

scholarly journals Integrating Data Science Tools into a Graduate Level Data Management Course

2018 ◽  
Vol 7 (3) ◽  
pp. e1152 ◽  
Author(s):  
Pete Pascuzzi ◽  
◽  
Megan Sapp Nelson ◽  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Graduate Level ◽  
Level Data

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 Big Data Management, Data Science and Data Analytics: What is it and Where— An Educational in Indian Perspective

2019 ◽  
Vol 8 (12) ◽  
pp. 1231-1236
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Science ◽  
Data Sets ◽  
Current Trends ◽  
Important Challenge ◽  
General Data ◽  
Theoretical Paper ◽  
Computational Systems ◽  
Indian Perspective

Large object normally treated as ‘Big’. It is a fact that Data is the raw information and content. Technology is rapidly changing emerging and today social media is very much popular and broken all the geographical boundaries. Big data is a concept and procedure which is deals with the data sets which are so large and in which traditional data processing become tough and eventually applications are inadequate. Analysis, capture, sharing, storage, visualization, querying, information etc in general data management principles become important challenge. Hence data sets having complexity and huge sizes suffer in adequacy. Business Intelligence is a related branch and accountable for the descriptive statistics with soaring information compactness to measure things, identify trends and so on. Data science approaches is deals with the quantitative analysis of data by using methods of statistical learning. It is an approach and combines classical statistical methods including progress in computational systems along with machine learning. This is a theoretical paper depicted current trends and issues of data science and big data. Moreover paper is also describes the potential and available programs in the field. Paper is also proposed and possible programs in the field.

Doing Harm

2019 ◽  
pp. 197-228
Author(s):  
Gary Smith ◽  
Jay Cordes
Keyword(s):  
Big Data ◽  
Data Science ◽  
Credit Cards ◽  
Golden Rule ◽  
Big Business ◽  
Good Data ◽  
Big Brother ◽  
Surveillance Cameras ◽  
Do So

An unfortunate reality in the age of big data is Big Brother monitoring us incessantly. Big Brother is indeed watching, but it is big business as well as big government collecting detailed information about everything we do so that they can predict our actions and manipulate our behavior. Big business and big government monitor our credit cards, checking accounts, computers, and telephones, watch us on surveillance cameras, and purchase data from firms dedicated to finding out everything they can about each and every one of us. Good data scientists proceed cautiously, respectful of our rights and our privacy. The Golden Rule applies to data science: treat others as you would like to be treated.

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.

Sign in / Sign up

Export Citation Format

Share Document