Literaturnachweis - Detailanzeige
| Autor/in | Jordan, Altricia |
|---|---|
| Titel | Integrating Data Science with Reliability Engineering: A Study of Crucial Knowledge and Skills |
| Quelle | (2023), (263 Seiten)
PDF als Volltext Ph.D. Dissertation, University of Arkansas at Little Rock |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| ISBN | 979-8-3795-3758-6 |
| Schlagwörter | Hochschulschrift; Dissertation; Data Science; Training; Scientists; Reliability; Engineering; Technical Occupations; Skill Development |
| Abstract | Data science, as a discipline can be used in any area. However, in order to utilize data science techniques, data scientist must be taught domain knowledge, referred to as a partner discipline, in the area with which the techniques are to be utilized. Using a quantitative analysis of publicly available information and survey methodology, this research developed recommendations for training data scientist in the partner discipline of reliability engineering. A text analysis was performed on reliability engineering job requisitions resulting in a set of knowledge and skills topics that organizations desire in reliability engineering hires. Using the results of the text analysis along with knowledge from other sources, a survey was developed then administered to reliability engineering practitioners. The survey was taken by 50 reliability engineering practitioners over two major collection events, the 13th Annual Huntsville Society of Reliability Engineers Training Summit and the 68th Annual RAMS. The survey of reliability engineering practitioners revealed that a large percentage of the topics derived from the text analysis were utilized by the majority of reliability engineers lending credibility to results for both the text analysis and the survey. In lieu of a standardized curriculum, colleges and university reliability engineering curriculums were analyzed to determine a baseline in reliability engineering knowledge. This research concluded that the formal education training by colleges and universities in reliability engineering has no specific set of knowledge being taught by programs and that the knowledge being taught may not be adequate training for meeting the needs of organizations. In order to eliminate duplication in training, data science curriculums were analyzed to determine the knowledge that data scientist possess. Those results were then compared to the survey results, which were used for reliability engineering domain knowledge. This research concluded that with so little overlap in engineering, as expected, the domain knowledge needed to apply data science to reliability engineering would be lacking without the additional training proposed in this research. Conversely, data science curriculums possess the data science techniques that reliability engineers perform in their everyday work. Using the text analysis, survey results, and data science curriculums analysis, nineteen topics were found in reliability engineering that would serve as the domain knowledge needed for data scientist to be effective in reliability engineering. Also, this research developed a list of data science topics that current reliability engineers should be trained on to be more effective in their role. The study concludes that there is interest in data science by reliability engineers in general whether that is having more data science skills for themselves or having a teammate with data science skills. Therefore, there is a need for developing the reliability engineering training for data science. There is also a need for developing data science training for reliability engineers. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided). |
| Anmerkungen | ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
Literaturbeschaffung und Bestandsnachweise in Bibliotheken prüfen
Permalink als QR-Code
Inhalt auf sozialen Plattformen teilen (nur vorhanden, wenn Javascript eingeschaltet ist)
Teile diese Seite:
