Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives

2021 ◽  
pp. 123-158
Author(s):  
Bao Hoang Nguyen ◽  
Valentin Zelenyuk
Keyword(s):  
Operations Research ◽  
Data Science ◽  
Hospital Efficiency ◽  
Dea Analysis

scholarly journals Development of Smart Semiconductor Manufacturing: Operations Research and Data Science Perspectives

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108419-108430 ◽  
Author(s):  
Marzieh Khakifirooz ◽  
Mahdi Fathi ◽  
Kan Wu
Keyword(s):  
Operations Research ◽  
Semiconductor Manufacturing ◽  
Data Science ◽  
Manufacturing Operations

The information quality framework for evaluating data science programs

2018 ◽  
Vol 02 (02) ◽  
pp. 1730001
Author(s):  
Shirley Y. Coleman ◽  
Ron S. Kenett
Keyword(s):  
Best Practices ◽  
Operations Research ◽  
Data Science ◽  
Information Quality ◽  
Science Content ◽  
Course Planning ◽  
Wide Range ◽  
Quality Framework ◽  
Teaching Faculty ◽  
Computer Science Faculty

Designing a new Analytics programF requires not only identifying needed courses, but also tying the courses together into a cohesive curriculum with an overriding theme. Such a theme helps to determine the proper sequencing of courses and create a coherent linkage between different courses often taught by faculty staff from different domains. It is common to see a program with some courses taught by computer science faculty, other courses taught by faculty and staff from the statistics department, and others from operations research, economics, information systems, marketing or other disciplines. Applying an overriding theme not only helps students organize their learning and course planning, but it also helps the teaching faculty in designing their materials and choosing terminology. The InfoQ framework introduced by Kenett and Shmueli provides a theme that focuses the attention of faculty and students on the important question of the value of data and its analysis with flexibility that accommodates a wide range of data analysis topics. In this chapter, we review a number of programs focused on analytics and data science content from an InfoQ perspective. Our goal is to show, with examples, how the InfoQ dimensions are addressed in existing programs and help identify best practices for designing and improving such programs. We base our assessment on information derived from the program’s web site.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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