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Data science technology course: The design, assessment and computing environment perspectives.

Azlan Ismail1,2, Sofianita Mutalib2,3, Haryani Haron2

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This course integrates Data Science and Big Data skills for Master's students, utilizing a Hadoop cluster for enhanced learning. The curriculum and platform ensure students gain practical experience with large datasets and advanced computing tools.

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Area of Science:

  • Data Science Education
  • Big Data Technologies
  • Computational Science

Background:

  • Master of Data Science programs require specialized Big Data skills.
  • Existing curricula may lack comprehensive training in Big Data platforms and tools.
  • The need for accessible, cluster-based computing is crucial for practical data science education.

Purpose of the Study:

  • To present the design and implementation of a Data Science Technology course.
  • To address the integration of Data Science and Big Data skill requirements.
  • To establish a stable and accessible cluster-based computing platform for postgraduate students.

Main Methods:

  • Course design and assessment strategies were developed.
  • A Hadoop cluster was configured and deployed.
  • A flexible accessibility strategy was implemented for the computing platform.

Main Results:

  • The course incorporates innovative elements covering key Data Science knowledge areas and job skills.
  • A stable Hadoop cluster with flexible accessibility was successfully deployed.
  • The cluster demonstrated capability to handle datasets larger than those used in prior semesters.

Conclusions:

  • The Data Science Technology course effectively bridges Data Science and Big Data competencies.
  • The implemented Hadoop cluster provides a robust and accessible platform for advanced data processing.
  • The course and platform are scalable and adaptable for future improvements and larger datasets.