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Data Science Technology Course: The Design, Assessment and Computing Environment Perspectives

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Title: Data Science Technology Course: The Design, Assessment and Computing Environment Perspectives
Language: English
Authors: Ismail, Azlan (ORCID 0000-0002-2535-6359), Mutalib, Sofianita, Haron, Haryani
Source: Education and Information Technologies. Aug 2023 28(8):10209-10234.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 26
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Data Science, Graduate Study, Masters Programs, Graduate Students, Curriculum Design, Data Analysis, Computer Uses in Education
DOI: 10.1007/s10639-022-11558-8
ISSN: 1360-2357
1573-7608
Abstract: This article discusses the key elements of the Data Science Technology course offered to postgraduate students enrolled in the Master of Data Science program. This course complements the existing curriculum by providing the skills to handle the Big Data platform and tools, in addition to data science activities. We tackle the discussion about this course based on three main requirements, which are related to the need to exploit the key skills from two dimensions, namely, Data Science and Big Data, and the need for a cluster-based computing platform and its accessibility. We address these requirements by presenting the course design and its assessments, the configuration of the computing platform, and the strategy to enable flexible accessibility. In terms of course design, the offered course contributes to several innovative elements and has covered multiple key areas of the data science body of knowledge and multiple quadrants of the job and skills matrix. In the case of the computing platform, a stable deployment of a Hadoop cluster with flexible accessibility, triggered by the pandemic situation, has been established. Furthermore, through our experience with the implementation of the cluster, it has shown the ability of the cluster to handle computing problems with a larger dataset than the one used for the semesters within the scope of the study. We also provide some reflections and highlight future improvements.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1388244
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: This article discusses the key elements of the Data Science Technology course offered to postgraduate students enrolled in the Master of Data Science program. This course complements the existing curriculum by providing the skills to handle the Big Data platform and tools, in addition to data science activities. We tackle the discussion about this course based on three main requirements, which are related to the need to exploit the key skills from two dimensions, namely, Data Science and Big Data, and the need for a cluster-based computing platform and its accessibility. We address these requirements by presenting the course design and its assessments, the configuration of the computing platform, and the strategy to enable flexible accessibility. In terms of course design, the offered course contributes to several innovative elements and has covered multiple key areas of the data science body of knowledge and multiple quadrants of the job and skills matrix. In the case of the computing platform, a stable deployment of a Hadoop cluster with flexible accessibility, triggered by the pandemic situation, has been established. Furthermore, through our experience with the implementation of the cluster, it has shown the ability of the cluster to handle computing problems with a larger dataset than the one used for the semesters within the scope of the study. We also provide some reflections and highlight future improvements.
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