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Master of Data Analytics - Major in Software Engineering

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

 
AQF Level Level 9
Duration 2 Years (4 Trimesters) Full-Time or Equivalent Part-Time
Location Melbourne & Sydney Campus
Intake March, July, November

  • Overview
  • Course details
  • Accreditation
  • Learning Outcomes
  • Credit Transfer
  • Entry Requirements
  • Fees and charges
  • How to apply

About the major

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.

Apply Now

Course Details

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.

Course Structure

  • Cognate
  • Non Cognate

Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MDA511 Mathematical and Statistical Methods
  3. MDA512 Data Science
  4. MDA513 ICT Practices
Trimester 2
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA523 Software Engineering Fundamentals
  3. Elective

Year 2

Trimester 3
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

 

  1. IoT Data Analytics
  2. MDA541 IOT and Sensor Networks
  3. MDA641 Smart Environments (Pre-requisite: MDA541 IOT and Sensor Networks)
  4. MDA642 IoT Data Analytics Platforms (Pre-requisite: MDA541 IOT and Sensor Networks)
  1. Cloud Networks
  2. MN503 Overview of Internetworking
  3. ME605 Cloud Engineering (Pre-requisite: MN503 Overview of Internetworking)
  4. MN622 Software Defined Networking (Pre-requisite: MN503 Overview of Internetworking)

The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.

 

Non-Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MN404 Fundamentals of Operating Systems and Programming
  3. MN405 Data and Information Management
  4. MDA511 Mathematical and Statistical Methods
Trimester 2
  1. MDA512 Data Science (Pre-requisites*: MN404 Fundamentals of Operating Systems and Programming and MN405 Data and Information Management)
  2. MDA513 ICT Practices
  3. MDA523 Software Engineering Fundamentals

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  3. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)

 


Note

* 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:

  • The program is available each intake; however, some units of study are subject to quotas and minimum enrolment requirements.
  • Not all units of study are available every trimester, and changes in program structure occur from time to time.

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.

 

*Additional Fee

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.

 

Industry-Based Projects and Internships

  • MIT SITE Industry Project Selection Process

 

Professional accreditation and memberships

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.

Learning Outcomes

Graduates of this course will:

  • Apply modern software engineering principles and practices to meet the complex industry requirements in software systems for data analytics and other computing domains.
  • Analyse and evaluate existing and new software solutions in data analytics and other computing domains with demonstrated understanding of ethical standards and technological landscapes.
  • Design and develop quality-assured and secure software solutions in data analytics and other computing domains with cohesive understanding of software development life cycle.
  • Possess a body of knowledge taken from fields including Statistics, Applied Mathematics, Computer Ethics, Data Security and Privacy, Information Management and Machine Learning that is essential to both understanding and applications of contemporary and emerging Data Analytics principles and methodologies.
  • Be able to investigate and compare key data analytical technologies and assess their effectiveness for problem-solving and data protection in different application scenarios.
  • Demonstrate significant research, analysis and evaluation skills in the Data Analytics discipline, and exercise critical thinking and problem-solving ability to tackle complex real-world problems.
  • Be capable of independent professional work in data analytics project teams with an elevated level of autonomy and accountability.
  • Be able to collaborate and communicate effectively with different stakeholders of Data Analytics projects in a professional setting.

 

Credit Transfer

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

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:

  • Certified copy of relevant certificates and results for any studies that you have completed
  • Unit outlines
  • Credit Transfer Application Form – download the form here

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

Entry Requirement

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:

  • Institute Entry Requirement
  • MIT's Admissions Transparency
  • MIT Admissions Policy and Procedure

Study now, pay later - with FEE-HELP

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

 

Tuition Fees

For 2020-21 fee information visit Tuition Fee page.

 

Financial Assistance

Youth and student allowances

For details on Youth Allowance, Austudy and ABSTUDY, visit humanservices.gov.au

  • Admissions Policy and Procedure
  • Appeals and grievances procedures

 

Where to get further information

  • Tertiary Education Quality Standards Agency (TEQSA) (https://www.teqsa.gov.au)
  • Quality Indicators for Teaching and Learning (QILT) (https://www.qilt.edu.au)
  • Australian Qualifications Framework (https://www.aqf.edu.au)

 

How to Apply?

Learn more about the whole of institution information set here.

If you are not in Australia

All students who are not in Australia must apply through our registered agents.

Find a Registered Agent

If you are in Australia

All students who are currently in Australia may apply directly via our Portal.

Apply Now

 

Overview

About the major

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.

Apply Now

Course Details

Course Details

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.

Course Structure

  • Cognate
  • Non Cognate

Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MDA511 Mathematical and Statistical Methods
  3. MDA512 Data Science
  4. MDA513 ICT Practices
Trimester 2
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA523 Software Engineering Fundamentals
  3. Elective

Year 2

Trimester 3
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

 

  1. IoT Data Analytics
  2. MDA541 IOT and Sensor Networks
  3. MDA641 Smart Environments (Pre-requisite: MDA541 IOT and Sensor Networks)
  4. MDA642 IoT Data Analytics Platforms (Pre-requisite: MDA541 IOT and Sensor Networks)
  1. Cloud Networks
  2. MN503 Overview of Internetworking
  3. ME605 Cloud Engineering (Pre-requisite: MN503 Overview of Internetworking)
  4. MN622 Software Defined Networking (Pre-requisite: MN503 Overview of Internetworking)

The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.

 

Non-Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MN404 Fundamentals of Operating Systems and Programming
  3. MN405 Data and Information Management
  4. MDA511 Mathematical and Statistical Methods
Trimester 2
  1. MDA512 Data Science (Pre-requisites*: MN404 Fundamentals of Operating Systems and Programming and MN405 Data and Information Management)
  2. MDA513 ICT Practices
  3. MDA523 Software Engineering Fundamentals

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  3. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)

 


Note

* 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:

  • The program is available each intake; however, some units of study are subject to quotas and minimum enrolment requirements.
  • Not all units of study are available every trimester, and changes in program structure occur from time to time.

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.

 

*Additional Fee

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.

 

Industry-Based Projects and Internships

  • MIT SITE Industry Project Selection Process

 

Learning Outcomes

Learning Outcomes

Graduates of this course will:

  • Apply modern software engineering principles and practices to meet the complex industry requirements in software systems for data analytics and other computing domains.
  • Analyse and evaluate existing and new software solutions in data analytics and other computing domains with demonstrated understanding of ethical standards and technological landscapes.
  • Design and develop quality-assured and secure software solutions in data analytics and other computing domains with cohesive understanding of software development life cycle.
  • Possess a body of knowledge taken from fields including Statistics, Applied Mathematics, Computer Ethics, Data Security and Privacy, Information Management and Machine Learning that is essential to both understanding and applications of contemporary and emerging Data Analytics principles and methodologies.
  • Be able to investigate and compare key data analytical technologies and assess their effectiveness for problem-solving and data protection in different application scenarios.
  • Demonstrate significant research, analysis and evaluation skills in the Data Analytics discipline, and exercise critical thinking and problem-solving ability to tackle complex real-world problems.
  • Be capable of independent professional work in data analytics project teams with an elevated level of autonomy and accountability.
  • Be able to collaborate and communicate effectively with different stakeholders of Data Analytics projects in a professional setting.

 

Credit Transfer

Credit Transfer

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

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:

  • Certified copy of relevant certificates and results for any studies that you have completed
  • Unit outlines
  • Credit Transfer Application Form – download the form here

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

Accreditation

Professional accreditation and memberships

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.

Career Opportunities



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
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

Entry Requirement

Entry Requirement

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:

  • Institute Entry Requirement
  • MIT's Admissions Transparency
  • MIT Admissions Policy and Procedure

Fees & Charges

Study now, pay later - with FEE-HELP

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

 

Tuition Fees

For 2020-21 fee information visit Tuition Fee page.

 

Financial Assistance

Youth and student allowances

For details on Youth Allowance, Austudy and ABSTUDY, visit humanservices.gov.au

  • Admissions Policy and Procedure
  • Appeals and grievances procedures

 

Where to get further information

  • Tertiary Education Quality Standards Agency (TEQSA) (https://www.teqsa.gov.au)
  • Quality Indicators for Teaching and Learning (QILT) (https://www.qilt.edu.au)
  • Australian Qualifications Framework (https://www.aqf.edu.au)

 

How To Apply

How to Apply?

Learn more about the whole of institution information set here.

If you are not in Australia

All students who are not in Australia must apply through our registered agents.

Find a Registered Agent

If you are in Australia

All students who are currently in Australia may apply directly via our Portal.

Apply Now

 


 


AQF Level
Level 9


Duration
2 Years (4 Trimesters) Full-Time


Location
Melbourne & Sydney Campus


CRICOS Code
102711J (VIC)
102710K (NSW)


Intake
March, July, November

 
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

  • Overview
  • Course details
  • Accreditation
  • Learning Outcomes
  • Credit Transfer
  • Entry Requirements
  • Fees and charges
  • How to apply

About the major

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.

Apply Now

Course Details

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.

Course Structure

  • Cognate
  • Non Cognate

Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MDA511 Mathematical and Statistical Methods
  3. MDA512 Data Science
  4. MDA513 ICT Practices
Trimester 2
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA523 Software Engineering Fundamentals
  3. Elective

Year 2

Trimester 3
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

 

  1. IoT Data Analytics
  2. MDA541 IOT and Sensor Networks
  3. MDA641 Smart Environments (Pre-requisite: MDA541 IOT and Sensor Networks)
  4. MDA642 IoT Data Analytics Platforms (Pre-requisite: MDA541 IOT and Sensor Networks)
  1. Cloud Networks
  2. MN503 Overview of Internetworking
  3. ME605 Cloud Engineering (Pre-requisite: MN503 Overview of Internetworking)
  4. MN622 Software Defined Networking (Pre-requisite: MN503 Overview of Internetworking)

The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.

 

Non-Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MN404 Fundamentals of Operating Systems and Programming
  3. MN405 Data and Information Management
  4. MDA511 Mathematical and Statistical Methods
Trimester 2
  1. MDA512 Data Science (Pre-requisites*: MN404 Fundamentals of Operating Systems and Programming and MN405 Data and Information Management)
  2. MDA513 ICT Practices
  3. MDA523 Software Engineering Fundamentals

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  3. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)

 


Note

* 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:

  • The program is available each intake; however, some units of study are subject to quotas and minimum enrolment requirements.
  • Not all units of study are available every trimester, and changes in program structure occur from time to time.

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.

 

*Additional Fee

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.

 

Industry-Based Projects and Internships

  • MIT SITE Industry Project Selection Process

 

Professional accreditation and memberships

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.

Learning Outcomes

Graduates of this course will:

  • Apply modern software engineering principles and practices to meet the complex industry requirements in software systems for data analytics and other computing domains.
  • Analyse and evaluate existing and new software solutions in data analytics and other computing domains with demonstrated understanding of ethical standards and technological landscapes.
  • Design and develop quality-assured and secure software solutions in data analytics and other computing domains with cohesive understanding of software development life cycle.
  • Possess a body of knowledge taken from fields including Statistics, Applied Mathematics, Computer Ethics, Data Security and Privacy, Information Management and Machine Learning that is essential to both understanding and applications of contemporary and emerging Data Analytics principles and methodologies.
  • Be able to investigate and compare key data analytical technologies and assess their effectiveness for problem-solving and data protection in different application scenarios.
  • Demonstrate significant research, analysis and evaluation skills in the Data Analytics discipline, and exercise critical thinking and problem-solving ability to tackle complex real-world problems.
  • Be capable of independent professional work in data analytics project teams with an elevated level of autonomy and accountability.
  • Be able to collaborate and communicate effectively with different stakeholders of Data Analytics projects in a professional setting.

 

Credit Transfer

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

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:

  • Certified copy of relevant certificates and results for any studies that you have completed
  • Unit outlines
  • Credit Transfer Application Form – download the form here

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

Entry Requirement

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.)


International Students - English Language Requirement

English language proficiency testsResults Required
IELTS (Academic)Overall score 6.0 (no band less than 6.0) or equivalent

 

For further information, see the links below:

  • Institute Entry Requirement
  • MIT's Admissions Transparency
  • MIT Admissions Policy and Procedure

Tuition Fees

For 2020-21 fee information visit Tuition Fee page.

Where to get further information

  • Tertiary Education Quality Standards Agency (TEQSA) (https://www.teqsa.gov.au)
  • Quality Indicators for Teaching and Learning (QILT) (https://www.qilt.edu.au)
  • Australian Qualifications Framework (https://www.aqf.edu.au)

 

How to Apply?

Learn more about the whole of institution information set here.

If you are not in Australia

All students who are not in Australia must apply through our registered agents.

Find a Registered Agent

If you are in Australia

All students who are currently in Australia may apply directly via our Portal.

Apply Now

 

Overview

About the major

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.

Apply Now

Course Details

Course Details

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.

Course Structure

  • Cognate
  • Non Cognate

Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MDA511 Mathematical and Statistical Methods
  3. MDA512 Data Science
  4. MDA513 ICT Practices
Trimester 2
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA523 Software Engineering Fundamentals
  3. Elective

Year 2

Trimester 3
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

 

  1. IoT Data Analytics
  2. MDA541 IOT and Sensor Networks
  3. MDA641 Smart Environments (Pre-requisite: MDA541 IOT and Sensor Networks)
  4. MDA642 IoT Data Analytics Platforms (Pre-requisite: MDA541 IOT and Sensor Networks)
  1. Cloud Networks
  2. MN503 Overview of Internetworking
  3. ME605 Cloud Engineering (Pre-requisite: MN503 Overview of Internetworking)
  4. MN622 Software Defined Networking (Pre-requisite: MN503 Overview of Internetworking)

The Course Coordinator may approve another elective from 500 or 600 level units, subject to meeting pre-requisites.

 

Non-Cognate Study Plan

Year 1

Trimester 1
  1. AIM100 Academic Integrity Module
  2. MN404 Fundamentals of Operating Systems and Programming
  3. MN405 Data and Information Management
  4. MDA511 Mathematical and Statistical Methods
Trimester 2
  1. MDA512 Data Science (Pre-requisites*: MN404 Fundamentals of Operating Systems and Programming and MN405 Data and Information Management)
  2. MDA513 ICT Practices
  3. MDA523 Software Engineering Fundamentals

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)
  2. MDA622 Human-Computer Interaction Design (Pre-requisite: MDA523 Software Engineering Fundamentals
  3. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)

 


Note

* 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:

  • The program is available each intake; however, some units of study are subject to quotas and minimum enrolment requirements.
  • Not all units of study are available every trimester, and changes in program structure occur from time to time.

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.

 

*Additional Fee

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.

 

Industry-Based Projects and Internships

  • MIT SITE Industry Project Selection Process

 

Learning Outcomes

Learning Outcomes

Graduates of this course will:

  • Apply modern software engineering principles and practices to meet the complex industry requirements in software systems for data analytics and other computing domains.
  • Analyse and evaluate existing and new software solutions in data analytics and other computing domains with demonstrated understanding of ethical standards and technological landscapes.
  • Design and develop quality-assured and secure software solutions in data analytics and other computing domains with cohesive understanding of software development life cycle.
  • Possess a body of knowledge taken from fields including Statistics, Applied Mathematics, Computer Ethics, Data Security and Privacy, Information Management and Machine Learning that is essential to both understanding and applications of contemporary and emerging Data Analytics principles and methodologies.
  • Be able to investigate and compare key data analytical technologies and assess their effectiveness for problem-solving and data protection in different application scenarios.
  • Demonstrate significant research, analysis and evaluation skills in the Data Analytics discipline, and exercise critical thinking and problem-solving ability to tackle complex real-world problems.
  • Be capable of independent professional work in data analytics project teams with an elevated level of autonomy and accountability.
  • Be able to collaborate and communicate effectively with different stakeholders of Data Analytics projects in a professional setting.

 

Credit Transfer

Credit Transfer

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

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:

  • Certified copy of relevant certificates and results for any studies that you have completed
  • Unit outlines
  • Credit Transfer Application Form – download the form here

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

Accreditation

Professional accreditation and memberships

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.

Career Opportunities



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
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

Entry Requirement

Entry Requirement

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.)


International Students - English Language Requirement

English language proficiency testsResults Required
IELTS (Academic)Overall score 6.0 (no band less than 6.0) or equivalent

 

For further information, see the links below:

  • Institute Entry Requirement
  • MIT's Admissions Transparency
  • MIT Admissions Policy and Procedure

Fees & Charges

Tuition Fees

For 2020-21 fee information visit Tuition Fee page.

Where to get further information

  • Tertiary Education Quality Standards Agency (TEQSA) (https://www.teqsa.gov.au)
  • Quality Indicators for Teaching and Learning (QILT) (https://www.qilt.edu.au)
  • Australian Qualifications Framework (https://www.aqf.edu.au)

 

How To Apply

How to Apply?

Learn more about the whole of institution information set here.

If you are not in Australia

All students who are not in Australia must apply through our registered agents.

Find a Registered Agent

If you are in Australia

All students who are currently in Australia may apply directly via our Portal.

Apply Now

 


 

Got questions?

Our friendly course advisors are ready to help. Call today 1800 648 669.

  1800 648 669

We’re open Mon – Fri, 9 am – 5 pm, excluding Australian Holidays or you can email us anytime.


Disclaimer

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.

 

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