A Data Science Approach to Modelling a Manufacturing Facility's Electrical Energy Profile from Plant Production Data

2018 ◽  
Author(s):  
Konrad Mulrennan ◽  
John Donovan ◽  
David Tormey ◽  
Russell Macpherson
Keyword(s):  
Data Science ◽  
Electrical Energy ◽  
Plant Production ◽  
Production Data ◽  
Energy Profile ◽  
Science Approach

Analyzing and Predicting Colombian Undergrads Performance on Saber-Pro Test: A Data Science Approach

2021 ◽  
pp. 81-99
Author(s):  
Eugenia Arrieta Rodríguez ◽  
Paula María Almonacid ◽  
Santiago Cortés ◽  
Rafael Deaguas ◽  
Nohora Diaz ◽  
...  
Keyword(s):  
Data Science ◽  
Science Approach

scholarly journals ADVANCING AN INTERDISCIPLINARY SCIENCE OF AGING THROUGH A PRACTICE-BASED DATA SCIENCE APPROACH

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S480-S480
Author(s):  
Robert Lucero ◽  
Ragnhildur Bjarnadottir
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Older Adults ◽  
Text Mining ◽  
Language Processing ◽  
Fall Risk ◽  
Data Science ◽  
Care Quality ◽  
Science Approach ◽  
Hospitalized Older Adults

Abstract Two hundred and fifty thousand older adults die annually in United States hospitals because of iatrogenic conditions (ICs). Clinicians, aging experts, patient advocates and federal policy makers agree that there is a need to enhance the safety of hospitalized older adults through improved identification and prevention of ICs. To this end, we are building a research program with the goal of enhancing the safety of hospitalized older adults by reducing ICs through an effective learning health system. Leveraging unique electronic data and healthcare system and human resources at the University of Florida, we are applying a state-of-the-art practice-based data science approach to identify risk factors of ICs (e.g., falls) from structured (i.e., nursing, clinical, administrative) and unstructured or text (i.e., registered nurse’s progress notes) data. Our interdisciplinary academic-clinical partnership includes scientific and clinical experts in patient safety, care quality, health outcomes, nursing and health informatics, natural language processing, data science, aging, standardized terminology, clinical decision support, statistics, machine learning, and hospital operations. Results to date have uncovered previously unknown fall risk factors within nursing (i.e., physical therapy initiation), clinical (i.e., number of fall risk increasing drugs, hemoglobin level), and administrative (i.e., Charlson Comorbidity Index, nurse skill mix, and registered nurse staffing ratio) structured data as well as patient cognitive, environmental, workflow, and communication factors in text data. The application of data science methods (i.e., machine learning and text-mining) and findings from this research will be used to develop text-mining pipelines to support sustained data-driven interdisciplinary aging studies to reduce ICs.

A Network Data Science Approach to People Analytics

2019 ◽  
Vol 32 (2) ◽  
pp. 28-51 ◽  
Author(s):  
Nan Wang ◽  
Evangelos Katsamakas
Keyword(s):  
Power Law ◽  
Data Science ◽  
Weighted Graph ◽  
Future Research ◽  
Network Data ◽  
Development Organization ◽  
Centrality Metrics ◽  
Science Approach ◽  
Future Research Directions ◽  
Using Data

The best companies compete with people analytics. They maximize the business value of their people to gain competitive advantage. This article proposes a network data science approach to people analytics. Using data from a software development organization, the article models developer contributions to project repositories as a bipartite weighted graph. This graph is projected into a weighted one-mode developer network to model collaboration. Techniques applied include centrality metrics, power-law estimation, community detection, and complex network dynamics. Among other results, the authors validate the existence of power-law relationships on project sizes (number of developers). As a methodological contribution, the article demonstrates how network data science can be used to derive a broad spectrum of insights about employee effort and collaboration in organizations. The authors discuss implications for managers and future research directions.

A data science approach to predicting patient aggressive events in a psychiatric hospital

2018 ◽  
Vol 268 ◽  
pp. 217-222 ◽  
Author(s):  
Robert Suchting ◽  
Charles E. Green ◽  
Stephen M. Glazier ◽  
Scott D. Lane
Keyword(s):  
Psychiatric Hospital ◽  
Data Science ◽  
Science Approach

scholarly journals Big data analytics of corporate internet disclosures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed A.K. Basuony ◽  
Ehab K.A. Mohamed ◽  
Ahmed Elragal ◽  
Khaled Hussainey
Keyword(s):  
Social Media ◽  
Firm Size ◽  
Data Science ◽  
Big Data Analytics ◽  
Corporate Disclosure ◽  
Content Type ◽  
Science Approach ◽  
The Usa ◽  
Corporate Office ◽  
The Uk

Purpose This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA. Design/methodology/approach A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA. Findings The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure. Practical implications This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process. Originality/value To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.

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