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Unit (2020)

Information on this page, including unit offerings, is from the 2020 academic year.

Data Science Applications (ICT583)

Organisational Unit Information Technology, Mathematics and Statistics
Credit Points 3
Availability MURDOCH: S1-internal, S2-internal
OUA: S2-external
Teaching Timetables Murdoch S1
Murdoch S2
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 structured activities in the form of lectures and tutorials. The assessments throughout the semester include individual assignments and final on-line examination. The combination of lectures and tutorials 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 tutorial tasks. Students will be able to access, through LMS, a variety of learning materials, such as articles and videos, which will help them acquire the knowledge of the unit at their own pace.
Assessment Mid-term assignment - 30% Data analysis project - 30% Online examination - 40%
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 an online examination.
Appears in these Courses/Majors:
see individual structures for context
Graduate Certificate in Information Technology (GradCertIT)
Graduate Certificate in Information Technology (OUA) (GradCertIT)
Graduate Diploma in Information Technology [New in 2020]
Master of Health Administration, Policy and Leadership (MHAPL) [New in 2016]
Master of Information Technology (MIT)
Internet Access RequirementsMurdoch 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.

Contacts

Unit Coordinator
ICT583
Dr Guanjin Wang
Lecturer in Information Technology

Murdoch Campus
t: 9360 7351
e: Guanjin.Wang@murdoch.edu.au
o: 245.1.028 - Science and Computing, Murdoch Campus
Unit Contacts
ICT583

MURDOCH: S1-Internal
Dr Guanjin Wang
Lecturer in Information Technology

Murdoch Campus
t: 9360 7351
e: Guanjin.Wang@murdoch.edu.au
o: 245.1.028 - Science and Computing, Murdoch Campus
ICT583

MURDOCH: S2-Internal
OUA: S2-External
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