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Master of Data Analytics

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 course

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 skillset will be relevant and in-demand, opening 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 several 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 MDA692 Data Analytics Capstone Project Unit.

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 skill. You will also get access to MIT’s Library of the latest books/ eBooks and hundreds of Academic online publications that will keep you knowledgeable of the latest and greatest. With 24 hours of 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.

Apply Now

Course Details

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 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. MDA521 Data Security and Privacy
  2. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  3. Elective

Year 2

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

 


Electives

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.

 

  1. IoT Data Analytics Specialisation
  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 Specialisation
  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)
No Specialisation

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. MDA521 Data Security and Privacy

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)/li>
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

  1. MDA541 IOT and Sensor Networks
  2. MN503 Overview of Internetworking

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

___________

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.

Specialisation into Iot Data Analytics and Cloud Networks is not available for non-cognate stream students.

 

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 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.

Learning Outcomes

Graduates of this course will:

  • 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 course

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 skillset will be relevant and in-demand, opening 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 several 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 MDA692 Data Analytics Capstone Project Unit.

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 skill. You will also get access to MIT’s Library of the latest books/ eBooks and hundreds of Academic online publications that will keep you knowledgeable of the latest and greatest. With 24 hours of 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.

Apply Now

Course Details

Course Details

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 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. MDA521 Data Security and Privacy
  2. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  3. Elective

Year 2

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

 


Electives

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.

 

  1. IoT Data Analytics Specialisation
  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 Specialisation
  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)
No Specialisation

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. MDA521 Data Security and Privacy

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)/li>
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

  1. MDA541 IOT and Sensor Networks
  2. MN503 Overview of Internetworking

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

___________

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.

Specialisation into Iot Data Analytics and Cloud Networks is not available for non-cognate stream students.

 

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:

  • 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 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.

Career Opportunities

Career Opportunities

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.

_________________

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

 


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 course

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 skillset will be relevant and in-demand, opening 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 several 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 MDA692 Data Analytics Capstone Project Unit.

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 skill. You will also get access to MIT’s Library of the latest books/ eBooks and hundreds of Academic online publications that will keep you knowledgeable of the latest and greatest. With 24 hours of 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.

Apply Now

Course Details

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 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. MDA521 Data Security and Privacy
  2. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  3. Elective

Year 2

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

 


Electives

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.

 

  1. IoT Data Analytics Specialisation
  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 Specialisation
  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)
No Specialisation

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. MDA521 Data Security and Privacy

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)/li>
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

  1. MDA541 IOT and Sensor Networks
  2. MN503 Overview of Internetworking

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

___________

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.

Specialisation into Iot Data Analytics and Cloud Networks is not available for non-cognate stream students.

 

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 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.

Learning Outcomes

Graduates of this course will:

  • 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 course

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 skillset will be relevant and in-demand, opening 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 several 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 MDA692 Data Analytics Capstone Project Unit.

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 skill. You will also get access to MIT’s Library of the latest books/ eBooks and hundreds of Academic online publications that will keep you knowledgeable of the latest and greatest. With 24 hours of 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.

Apply Now

Course Details

Course Details

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 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. MDA521 Data Security and Privacy
  2. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  3. Elective

Year 2

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

 


Electives

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.

 

  1. IoT Data Analytics Specialisation
  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 Specialisation
  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)
No Specialisation

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. MDA521 Data Security and Privacy

Year 2

Trimester 3
  1. MDA522 Artificial Intelligence (Pre-requisite: MDA512 Data Science)
  2. MDA611 Predictive Analytics (Pre-requisite: MDA511 Mathematical and Statistical Methods)/li>
  3. MDA691 Project Management and Research Methods (Pre-requisite: 4 Core units)
Trimester 4
  1. MDA621 Software Practice for Big Data Analytics (Pre-requisite: MDA512 Data Science)
  2. MDA692 Data Analytics Capstone Project (Pre-requisite: MDA691 Project Management and Research Methods)
  3. Elective

 


Electives

The following electives are currently offered.

  1. MDA541 IOT and Sensor Networks
  2. MN503 Overview of Internetworking

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

___________

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.

Specialisation into Iot Data Analytics and Cloud Networks is not available for non-cognate stream students.

 

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:

  • 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 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.

Career Opportunities

Career Opportunities

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.

_________________

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

 


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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