18 monthsProgramme duration
56,120 AUDTuition Fee/year
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
Other
Study Level
Masters
Study Mode
On Campus
COMP8910 Data Mining (Intensive) replaced with COMP8410
COMP8930 Data Wrangling (intensive) replaced with COMP8430
COMP7240 Introduction to Database Concepts (intensive) replaced with COMP6240
COMP7230 Introduction to Programming for Data Scientists (intensive) replaced with COMP6730
COMP6990 Document Analysis (intensive) replaced with COMP6490
COMP8920 Neural Networks, Deep Learning and Bio-inspired Computing (intensive) replaced with COMP8420
The Master of Applied Data Analytics is a 1.5 year full-time (or equivalent part-time) degree that provides students with:
- Exposure to best practice in data analytics.
- Cutting edge courses in areas of relevance to data analytics practitioners.
- An opportunity to deepen knowledge in one of the three areas of computation, statistics, or social science.
- Professional development for practicing data analytics professionals.
- The opportunity to undertake research of professional relevance.
The program is taught in semester mode, and for domestic students the program is also offered in intensive blended mode. Students studying in intensive blended mode are expected to be enrolled part-time. The intensive blended course delivery mode is designed to suit working students who take leave from work (or other commitments) to attend an intensive 1 week of full time learning on campus in the middle of the course, and study remotely for the rest of the course. The intensive blended course delivery mode comprises: 4 weeks of online study, 1 full time week of face to face learning on campus, followed by a further 4 weeks of online study.
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
Other
Study Level
Masters
Study Mode
On Campus
COMP8910 Data Mining (Intensive) replaced with COMP8410
COMP8930 Data Wrangling (intensive) replaced with COMP8430
COMP7240 Introduction to Database Concepts (intensive) replaced with COMP6240
COMP7230 Introduction to Programming for Data Scientists (intensive) replaced with COMP6730
COMP6990 Document Analysis (intensive) replaced with COMP6490
COMP8920 Neural Networks, Deep Learning and Bio-inspired Computing (intensive) replaced with COMP8420
The Master of Applied Data Analytics is a 1.5 year full-time (or equivalent part-time) degree that provides students with:
- Exposure to best practice in data analytics.
- Cutting edge courses in areas of relevance to data analytics practitioners.
- An opportunity to deepen knowledge in one of the three areas of computation, statistics, or social science.
- Professional development for practicing data analytics professionals.
- The opportunity to undertake research of professional relevance.
The program is taught in semester mode, and for domestic students the program is also offered in intensive blended mode. Students studying in intensive blended mode are expected to be enrolled part-time. The intensive blended course delivery mode is designed to suit working students who take leave from work (or other commitments) to attend an intensive 1 week of full time learning on campus in the middle of the course, and study remotely for the rest of the course. The intensive blended course delivery mode comprises: 4 weeks of online study, 1 full time week of face to face learning on campus, followed by a further 4 weeks of online study.
Admission Requirements
- A Bachelor degree with Honours or international equivalent with a minimum GPA of 5.0/7.0
- A Bachelor degree or international equivalent with a minimum GPA of 5.0/7.0, plus at least 3 years of relevant work experience
The GPA for a Bachelor program will be calculated from (i) a completed Bachelor degree using all grades and/or (ii) a completed Bachelor degree using all grades other than those from the last semester (or equivalent study period) of the Bachelor degree. The higher of the two calculations will be used as the basis for admission.
Tuition fees
International
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