Big Data Analytics in the Higher Education: Need of the Future

2018 ◽  
pp. 23-28
Author(s):  
Praveen Mukhia Titimus
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Future

Big Data Analytics and Models

2020 ◽  
pp. 10-33
Author(s):  
Ferdi Sönmez ◽  
Ziya Nazım Perdahçı ◽  
Mehmet Nafiz Aydın
Keyword(s):  
Big Data ◽  
Important Implication ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Financial Econometrics ◽  
Optimization Models ◽  
Modern World ◽  
Financial Instability ◽  
The Future ◽  
Social Environmental

When uncertainty is regarded as a surprise and an event in the minds, it can be said that individuals can change the future view. Market, financial, operational, social, environmental, institutional and humanitarian risks and uncertainties are the inherent realities of the modern world. Life is suffused with randomness and volatility; everything momentous that occurs in the illustrious sweep of history, or in our individual lives, is an outcome of uncertainty. An important implication of such uncertainty is the financial instability engendered to the victims of different sorts of perils. This chapter is intended to explore big data analytics as a comprehensive technique for processing large amounts of data to uncover insights. Several techniques before big data analytics like financial econometrics and optimization models have been used. Therefore, initially these techniques are mentioned. Then, how big data analytics has altered the methods of analysis is mentioned. Lastly, cases promoting big data analytics are mentioned.

scholarly journals Decision support model to adopt big data analytics in higher education systems

2021 ◽  
Vol 8 (6) ◽  
pp. 67-78
Author(s):  
Adel Alkhalil ◽  
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Data Science ◽  
Big Data Analytics ◽  
Equation Modeling ◽  
Systematic Research ◽  
Education Systems ◽  
Higher Education Systems

Data science or specifically data analytics systems have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in the house. The collection, analysis, and visualization of such big data present a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the rationale and decision process for the adoption of big data analytics can be difficult. Such a knowledge-driven process requires a multitude of technical and organizational aspects that must be accounted for to ensure informed decisions are made. Existing research and development indicates that the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated framework referred to as the Technology Organization Environment (TOE) framework is proposed. The proposed framework is validated using structural equation modeling. Eleven determinants are confirmed that influence the TOE-driven framework for data analytics in HES. The result is expected to contribute to on-going research that attempts to address the complex and multidimensional challenge that relates to data science and analytics implementation in HES.

scholarly journals A functional framework based on big data analytics for smart farming

2021 ◽  
Vol 24 (3) ◽  
pp. 1772
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
New Products ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Time Production ◽  
Smart Farming ◽  
The Future

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

Big Data Analytics in Undergraduate Advertising Curricula

2022 ◽  
pp. 1958-1973
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Past Research ◽  
Data Driven ◽  
The World ◽  
Tools And Techniques ◽  
Personalized Advertising

The rapid ascent of data-driven advertising practices has allowed advertising professionals to develop highly-targeted and personalized advertising campaigns. The success of data-driven advertising relies on if future professionals are proficient with basics of Big Data analytics. However, past research of undergraduate advertising curricula around the world has shown that higher education institutions tend to fall behind in offering the most up-to-dated training for advertising students. Findings have shown that undergraduate advertising programs have slowly taken advantage of the potential of the data analytics tools and techniques. This trend is observed among higher education institutions around the world. Practical, research, and pedagogical implications are discussed.

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