| I am a(n) | Domestic student | International Student |
I am a(n)
| Domestic student | |
| International Student |
| AQF Level Level 9 | |
| Duration 2 Years (4 Trimesters) Full-Time or Equivalent Part-Time | |
| Location Melbourne & Sydney Campus | |
| Intake March, July, November |
The Master of Data Analytics - Major in Software Engineering has been developed in consultation with a broadly represented industry advisory panel. The Major in Software Engineering prepares students for a future career in multiple employment areas by capitalizing on the intersection of two fast growing fields of high demand, including Data Analytics and Software Engineering. Graduates of the major can play a crucial role in transforming future businesses in different industry domains by applying their data analytics and software engineering skills.
The Major in Software Engineering will prepare our students for the future market by studying units that focus on Software Engineering Fundamentals, Software Practice for Big Data Analytics, and Human-computer Interaction Design. They will gain in-depth understanding of essential Software Engineering principles, Software Development Lifecycle models, Software Quality Assurance and Testing methodologies, Human-Computer Interaction and to apply them in designing and developing robust and tested software artefacts and applications across a wide range of industries that deploy data analytics, and on projects in other application areas.
Industry Project
As part of Master of Data Analytics - Major in Software Engineering, you will undertake the final year MDA692 Data Analytics Capstone Project Unit working on a substantial software project.
MIT’s School of IT & Engineering has designed this unit to provide you with 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 complex real-world problems with technical and creative skills.
As an MIT student, you will have access to state-of-the-art labs and facilities. All of MIT’s labs are equipped with the latest software to hone your skills. You will also have access to MIT’s Library of the latest books, eBooks and hundreds of Academic online publications that will keep you knowledgeable about the latest and greatest. With 24 hours of digital access, you are never too far away from the latest news and journal articles 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 at MIT, you will not only learn from the best minds in the filed but can also 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. 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 enrol 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.
The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.
* 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.
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.
The Master of Data Analytics - Major in Software Engineering 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 - Major in Software Engineering is accredited by the Tertiary Education Quality and Standards Agency (TEQSA) - www.teqsa.gov.au.
Graduates of this course will:
Credit transfer provides 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 Recognition of Prior Learning.
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
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 the transfer from the non-cognate to the cognate stream based on evidence provided for recognition of prior learning in Information Technology.)
For further information, see the links below:
Students studying at MIT may be eligible for FEE-HELP. FEE-HELP is a loan scheme that assists eligible fee-paying students to pay their tuition fees. An eligible person may borrow up to the FEE-HELP limit to pay tuition fees over their lifetime.
For further information about FEE-HELP, click here or visit studyassist.gov.au
For 2020-21 fee information visit Tuition Fee page.
Youth and student allowances
For details on Youth Allowance, Austudy and ABSTUDY, visit humanservices.gov.au
Learn more about the whole of institution information set here.
All students who are not in Australia must apply through our registered agents.
All students who are currently in Australia may apply directly via our Portal.
The Master of Data Analytics - Major in Software Engineering has been developed in consultation with a broadly represented industry advisory panel. The Major in Software Engineering prepares students for a future career in multiple employment areas by capitalizing on the intersection of two fast growing fields of high demand, including Data Analytics and Software Engineering. Graduates of the major can play a crucial role in transforming future businesses in different industry domains by applying their data analytics and software engineering skills.
The Major in Software Engineering will prepare our students for the future market by studying units that focus on Software Engineering Fundamentals, Software Practice for Big Data Analytics, and Human-computer Interaction Design. They will gain in-depth understanding of essential Software Engineering principles, Software Development Lifecycle models, Software Quality Assurance and Testing methodologies, Human-Computer Interaction and to apply them in designing and developing robust and tested software artefacts and applications across a wide range of industries that deploy data analytics, and on projects in other application areas.
Industry Project
As part of Master of Data Analytics - Major in Software Engineering, you will undertake the final year MDA692 Data Analytics Capstone Project Unit working on a substantial software project.
MIT’s School of IT & Engineering has designed this unit to provide you with 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 complex real-world problems with technical and creative skills.
As an MIT student, you will have access to state-of-the-art labs and facilities. All of MIT’s labs are equipped with the latest software to hone your skills. You will also have access to MIT’s Library of the latest books, eBooks and hundreds of Academic online publications that will keep you knowledgeable about the latest and greatest. With 24 hours of digital access, you are never too far away from the latest news and journal articles 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 at MIT, you will not only learn from the best minds in the filed but can also 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. 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 enrol 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.
The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.
* 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.
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 provides 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 Recognition of Prior Learning.
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
The Master of Data Analytics - Major in Software Engineering 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 - Major in Software Engineering is accredited by the Tertiary Education Quality and Standards Agency (TEQSA) - www.teqsa.gov.au.
| AQF Level Level 7 | |
| Duration 3 Years (6 Trimesters) Full-Time or Equivalent Part-Time | |
| Location Melbourne & Sydney Campus | |
| VTAC Code 9470194722 (DFP) 9470194723 (IFP) | |
| UAC Code 570130 | |
| Intake March, July, November |
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 the transfer from the non-cognate to the cognate stream based on evidence provided for recognition of prior learning in Information Technology.)
For further information, see the links below:
Students studying at MIT may be eligible for FEE-HELP. FEE-HELP is a loan scheme that assists eligible fee-paying students to pay their tuition fees. An eligible person may borrow up to the FEE-HELP limit to pay tuition fees over their lifetime.
For further information about FEE-HELP, click here or visit studyassist.gov.au
For 2020-21 fee information visit Tuition Fee page.
Youth and student allowances
For details on Youth Allowance, Austudy and ABSTUDY, visit humanservices.gov.au
Learn more about the whole of institution information set here.
All students who are not in Australia must apply through our registered agents.
All students who are currently in Australia may apply directly via our Portal.
| AQF Level Level 8 | |
| Duration 2 Years (4 Trimesters) Full-Time | |
| Location Melbourne & Sydney Campus | |
| CRICOS Code 102711J (VIC) 102710K (NSW) | |
| Intake March, July, November |
The Master of Data Analytics - Major in Software Engineering has been developed in consultation with a broadly represented industry advisory panel. The Major in Software Engineering prepares students for a future career in multiple employment areas by capitalizing on the intersection of two fast growing fields of high demand, including Data Analytics and Software Engineering. Graduates of the major can play a crucial role in transforming future businesses in different industry domains by applying their data analytics and software engineering skills.
The Major in Software Engineering will prepare our students for the future market by studying units that focus on Software Engineering Fundamentals, Software Practice for Big Data Analytics, and Human-computer Interaction Design. They will gain in-depth understanding of essential Software Engineering principles, Software Development Lifecycle models, Software Quality Assurance and Testing methodologies, Human-Computer Interaction and to apply them in designing and developing robust and tested software artefacts and applications across a wide range of industries that deploy data analytics, and on projects in other application areas.
Industry Project
As part of Master of Data Analytics - Major in Software Engineering, you will undertake the final year MDA692 Data Analytics Capstone Project Unit working on a substantial software project.
MIT’s School of IT & Engineering has designed this unit to provide you with 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 complex real-world problems with technical and creative skills.
As an MIT student, you will have access to state-of-the-art labs and facilities. All of MIT’s labs are equipped with the latest software to hone your skills. You will also have access to MIT’s Library of the latest books, eBooks and hundreds of Academic online publications that will keep you knowledgeable about the latest and greatest. With 24 hours of digital access, you are never too far away from the latest news and journal articles 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 at MIT, you will not only learn from the best minds in the filed but can also 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. 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 enrol 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.
The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.
* 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.
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.
The Master of Data Analytics - Major in Software Engineering 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 - Major in Software Engineering is accredited by the Tertiary Education Quality and Standards Agency (TEQSA) - www.teqsa.gov.au.
Graduates of this course will:
Credit transfer provides 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 Recognition of Prior Learning.
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
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 the 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:
For 2020-21 fee information visit Tuition Fee page.
Learn more about the whole of institution information set here.
All students who are not in Australia must apply through our registered agents.
All students who are currently in Australia may apply directly via our Portal.
The Master of Data Analytics - Major in Software Engineering has been developed in consultation with a broadly represented industry advisory panel. The Major in Software Engineering prepares students for a future career in multiple employment areas by capitalizing on the intersection of two fast growing fields of high demand, including Data Analytics and Software Engineering. Graduates of the major can play a crucial role in transforming future businesses in different industry domains by applying their data analytics and software engineering skills.
The Major in Software Engineering will prepare our students for the future market by studying units that focus on Software Engineering Fundamentals, Software Practice for Big Data Analytics, and Human-computer Interaction Design. They will gain in-depth understanding of essential Software Engineering principles, Software Development Lifecycle models, Software Quality Assurance and Testing methodologies, Human-Computer Interaction and to apply them in designing and developing robust and tested software artefacts and applications across a wide range of industries that deploy data analytics, and on projects in other application areas.
Industry Project
As part of Master of Data Analytics - Major in Software Engineering, you will undertake the final year MDA692 Data Analytics Capstone Project Unit working on a substantial software project.
MIT’s School of IT & Engineering has designed this unit to provide you with 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 complex real-world problems with technical and creative skills.
As an MIT student, you will have access to state-of-the-art labs and facilities. All of MIT’s labs are equipped with the latest software to hone your skills. You will also have access to MIT’s Library of the latest books, eBooks and hundreds of Academic online publications that will keep you knowledgeable about the latest and greatest. With 24 hours of digital access, you are never too far away from the latest news and journal articles 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 at MIT, you will not only learn from the best minds in the filed but can also 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. 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 enrol 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.
The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.
* 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.
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 provides 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 Recognition of Prior Learning.
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
The Master of Data Analytics - Major in Software Engineering 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 - Major in Software Engineering is accredited by the Tertiary Education Quality and Standards Agency (TEQSA) - www.teqsa.gov.au.
| AQF Level Level 7 | |
| Duration 3 Years (6 Trimesters) Full-Time or Equivalent Part-Time | |
| Location Melbourne & Sydney Campus | |
| VTAC Code 9470194722 (DFP) 9470194723 (IFP) | |
| UAC Code 570130 | |
| Intake March, July, November |
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 the 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:
For 2020-21 fee information visit Tuition Fee page.
Learn more about the whole of institution information set here.
All students who are not in Australia must apply through our registered agents.
All students who are currently in Australia may apply directly via our Portal.
We’re open Mon – Fri, 9 am – 5 pm, excluding Australian Holidays or you can email us anytime.
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.