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Unit of Study ISYS1006 Introduction to Computing for Data Analytics (2024)

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Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

articulate and apply knowledge of frameworks, processes, and techniques of data analytics and incorporate with the principles and strategies of the business environment

apply analytical thinking on business needs assessment and analytical problem framing

apply practical knowledge and skills of Python programming to perform data preparation, exploration, and modelling tasks

effectively interpret and communicate insights from data analytics projects using appropriate data analytics techniques.

Teaching and assessment

Teaching method
Workshop 1 hour (Weekly)
Tutorial 2 hours (Weekly)
Assessment
Quiz20%
Report30%
Report and Recorded Presentation50%

Teaching method
Workshop 1 hour (Weekly)
Tutorial 2 hours (Weekly)
Assessment
Quiz20%
Report30%
Report and Recorded Presentation50%

Teaching method
Workshop 1 hour (Weekly)
Tutorial 2 hours (Weekly)
Assessment
Quiz20%
Report30%
Report and Recorded Presentation50%

Teaching method
Workshop 1 hour (Weekly)
Tutorial 2 hours (Weekly)
Assessment
Quiz20%
Report30%
Report and Recorded Presentation50%

Teaching method
Workshop 1 hour (Weekly)
Tutorial 2 hours (Weekly)
Assessment
Quiz20%
Report30%
Report and Recorded Presentation50%

Prescribed learning resources

Prescribed Texts
  • No prescribed texts.
Prescribed Resources/Equipment
  • No prescribed resources/equipment.

Prescribed Learning Resources may change in future Teaching Periods.

Fee information

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