Big data and analytics are impacting every organisation, from technology start-ups to multinational companies. The Master of Data Analytics (MDA) at MIT teaches you the skills to analyse massive amounts of structured and unstructured data to provide insights and help meet specific business needs and goals.
You will learn the science and technology of transforming data into knowledge from units such as Artificial Intelligence (AI), Data Science, Big Data Analytics and Visualisation, and through Elective units like IoT (Internet of Things) or Cloud Networks. A final year Industry-based Capstone Project provides you with a Work-Integrated Learning (WIL) opportunity that gives you real-world experience and enhances your job prospects.
As a Data Analyst or Data Scientist, your skill set will be relevant and in-demand, opening up a myriad of job opportunities in industries like banking and finance, media and communications, health, education, information technology, engineering, agriculture, and mining.
Our teaching staff have strong connections with industry; Professor Johnson Agbinya, Head of School, IT & Engineering is a published author in the subject, and Professor Paul Kwan has worked on research for a number of organisations as well as published more than 120 peer-reviewed articles in fields including data analytics and AI.
Industry Project
As part of Master of Data Analytics degree, you will need to undertake the final year MA692 Data Analytics Capstone Project Unit.
MIT’s School of IT & Engineering has designed this unit to provide you with the real world experience, working on a project for an industry client. They will provide you with a capstone final year project in a team environment including research, analysis and development. You will tackle real world complex problems with technical and creative skills.
As an MIT student, you will have access to the state-of-the-art labs and facilities. All MIT’s labs are equipped with the latest software to hone your skill. You will also get access to MIT’s Library of the latest books/ eBooks and hundreds of online Academic publications that will keep you knowledgeable of the latest and greatest. With 24 hour digital access, you are never too far away from the latest news and journals in the academic/research space.Learn from experts
MIT’s School of IT & Engineering attracts some of the best minds in the Data Analytics, Engineering and Networking field, like Data Science and Machine Learning expert Professor Paul Kwan and, Artificial Intelligence and Machine Learning expert Associate Professor Tony Jan.
As a student of MIT, you will not only learn from the minds as such but also can book a one-to-one appointment with any of your lecturers to discuss your ideas.
Each unit consists of 20 credit points. A full time study load is 60 credit points per trimester. MDA comprises of nine core units and three electives that may make up a specialisation such as IoT Data Analytics or Cloud Networks (see below). Non-cognate students who have gaps in their undergraduate program will be required to undertake MN404 and MN405 to ensure they meet the foundational knowledge for core units.
Students admitted into the cognate stream based on prior qualifications but wish to enroll in the non-cognate stream may transfer to the non-cognate stream with the written permission of the Course Coordinator.
The following electives are currently offered. Students should preferably select all 3 electives from the same specialisation. Specialisations in the below listed area are only available for Cognate Stream.
The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.
The following electives are currently offered.
The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.
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* The pre-requisite units specified for MDA512 are only for students in the non-cognate stream. Students in the cognate stream will have achieved the pre-requisite knowledge and skills in their prior qualification.
Specialisation into Iot Data Analytics and Cloud Networks is not available for non-cognate stream students.
Some remarks:
Program structures and units are subject to change through the process of regular course revision. There is no guarantee that every unit will be offered in any particular trimester.
It is mandatory for awarding of the Master of Data Analytics degree that the student undertake the final year project units: MDA691 Project Management and Research Methods, and MDA692 Data Analytics Capstone Project. If a third party is required to find a project for the student, the student will incur a fee. The industry based project is designed to provide students with real world experience, working for an industry client on a project focussed within their discipline of study.
Graduates of this course will:
Credit transfer provide students with credit for learning already achieved. Applicants are assessed on a case-by-case basis. Learn more about credit transfer.
Applications for credit transfer must be made before or during orientation and enrolment week.
Recognition of prior learning (RPL) allows students to gain credit towards their course based on their prior learning (including formal, informal and non‐formal learning). Applicants are assessed on a case-by-case basis.Learn more about credit transfer.
Applications for Recognition of Prior Learning must be made before or during orientation and enrolment week.
Applying for Credit Transfer/RPL
If you are seeking credit transfer for the same or similar unit/s previously completed at a different institute, you must submit the following supporting documents with your credit application:
For further information about credit transfer, click here
Submitting the Credit Transfer/ RPL Application
Once complete, scan and email your application via email to enquiries@mit.edu.au
As a graduate of the MDA, you will have knowledge and skills for employment in data analytics in industries such as banking and finance, media and communications, health, education, information technology, engineering, agriculture, and mining. Your knowledge, skills, and competencies in data science and analytics are expected to be highly sought after by organisations around the world. This aligns with the Jobs of Tomorrow 2020 Report by World Economic Forum.
According to the report (World Economic Forum, 2020, p.21), AI Specialist, Data Scientist, Data Engineer, Big Data Developer, Data Analyst, Analytics Specialist, Data Consultants, Insight Analyst, Business Intelligence Developer and Analytics Consultants are the top 10 most emerging jobs of 2020 and beyond.
MIT’s Master of Data Analytics will equip you with the required skills to apply for jobs in any of the above roles.
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ref:
World Economic Forum. (2020, January). Jobs of Tomorrow: Mapping Opportunity in the New Economy. https://www.weforum.org/reports/jobs-of-tomorrow-mapping-opportunity-in-the-new-economy
The Master of Data Analytics is a new course. It is not currently accredited by the Australian Computer Society. MIT intends to apply to the ACS for professional accreditation of the course.
The Master of Data Analytics is accredited by the Tertiary Education Quality and Standards Agency (TEQSA) - www.teqsa.gov.au.
For the cognate stream: An Australian Bachelor degree or equivalent in an Information and Communications Technology (ICT) discipline such as Computer Science, Computer Engineering, Information Technology and Software Engineering.
For the non-cognate stream: An Australian Bachelor degree or equivalent in a non-ICT discipline. (The course coordinator may approve transfer from the non-cognate to the cognate stream based on evidence provided for recognition of prior learning in Information Technology.)
| English language Proficiency tests | Results Required |
| IELTS (Academic) | Overall score 6.0 (no band less than 6.0) or equivalent |
For further information, see the links below:
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Even though MIT endeavours to provide correct information, this information may change throughout the year. You are strongly advised to verify the accuracy of this information with the relevant schools before making a decision.
