Information on this page, including unit offerings, is from the 2020 academic year.
Data Science Applications (ICT583)
|Organisational Unit||Information Technology, Mathematics and Statistics|
|Availability||MURDOCH: S1-internal, S2-internal|
|Teaching Timetables||Murdoch S1
|Description||The objective of this unit is to introduce important concepts in data science such as preparing data, visualizing data, extracting hidden patterns via exploratory data analysis, building predictive models and to help students put the learned knowledge into a real-world data science context. The unit will also develop an understanding of common applications in different domains that build upon data science.|
|Unit Learning Outcomes||Upon completion of this unit, students should be able to:
ULO 01: explain and use basic techniques relevant with data science;
ULO 02: identify and apply basic predictive modelling and data analysis methods that are appropriate to individual datasets and interpret the results;
ULO 03: describe the basic ideas and techniques behind a variety of modern data science applications;
ULO 04: apply knowledge in data pre-processing, visualization and analysis using R.
|Timetabled Learning Activities||Lectures: 1 x 2 hours per week; Workshop: 1 hour per week.|
|Unit Learning Experiences||This unit uses a mixture of structured activities in the form of lectures, semi-structured activities in the form of workshops and assessments in the form of individual and group assignments and final examination. The combination of lectures and workshops will allow students to initially understand the fundamental concepts and techniques/tools in data science applications and then put them in use in their weekly workshop tasks.
The use of blended learning in this unit will be prominent. Students will be able to access, through LMS, a variety of additional learning materials, such as articles and videos, which will help them acquire the knowledge of the unit at their own pace.
|Assessment||Participation - 10%
Research Paper Critique - 20%
Project and Presentation - 20%
Examination - 50%
|Prerequisites||Enrolment in an IT graduate course or permission of the Academic Chair.|
|Notes||A minimum of 3 hours per week of personal study for completing workshop activities, reading materials, assignments, private study and revision. Each student is required to complete assignments and sit the final examination.|
|Appears in these Courses/Majors:
see individual structures for context
|Internet Access Requirements||Murdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.|
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