Australian International Institute of Higher Education & Education

Master of Information Technology (Data Science)

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CRICOS Code

120468K

TEQSA Code

CRS1401856

Duration

104 Weeks

Intake

Feb | Jun | Oct

Study Mode

On Campus

Location

Sydney

AQF Level

Level 9

Broad Field

02 - Information Technology

Narrow Field

0201 - Computer Science

Detailed Field

020199 - Computer Science, n.e.c.

The Australian International Institute of Higher Education (AIIHE) has designed a profession-aligned Master of Information Technology (Data Science) program tailored to empower graduates for leadership roles as ICT senior executives, data analyst or ICT professional experts, with the specialised knowledge and skills in the area of data science and its associated fields.

In an era where all size-organisations trust heavily on information systems and Internet, relying on critical business and customer data that can help the organisation makes better decisions and gain a competitive advantage. As a business professional or ICT specialist, graduates not only understand how to analyse datasets but also interpret the data and sourcing the most relevant data which can be incorporated into the organisational business decision making system in a way of offering recommendation to decision makers and managers.

This program equips graduates with advanced technical proficiencies in the core knowledge of IT and data science technologies. By integrating knowledge and skills, the curriculum endows students with the tools and techniques required to gather, analyse, cleanse, design, and interpret data to confront challenges in the ever-evolving ICT space. The program is structured in alignment with the comprehensive ACS body of knowledge and added to other industry alignments such as the ACM-IEEE Computing Curricula.

At its core, the program integrates foundational units in information technology, such as software development, systems analysis design, database management systems, ethical and professional practice in IT, and project management. This foundation is further enriched by the specialised data science units such as statistical methods, data analytics, data management, modelling and mining, cloud computing and networks, ethical and legal issues in data science, and machine learning and optimisation techniques.

The pedagogy of the Master of Information Technology (Data Science) seamlessly blends practical insights from industry experts with a rigorous academic framework, establishing a robust understanding of general information technology and data science level.

The salient feature of this course is the inclusion of a Capstone Project subject, focused on tackling a contemporary issue and challenges pertaining to IT and data science. Here, students apply and integrate their knowledge to propose recommendations or solutions.

AIIIHE aims to prepare work-ready professionals. It is expected that graduates could start a career in Information Technology (Data Science) in positions such as:

  • Data Analyst
  • Data Scientist
  • Data Architect
  • Business Analyst
  • Data Analytics Consultant
  • Data Mining Analyst
  • Data Visualisation Analyst

Admission requirements are described in the AIIHE Admission Policy and Procedures and are summarised as follows. To be eligible for admissions to the Master of Information Technology (Data Science) applicants are required to hold the following:

  • Applicants must have completed an Australian bachelor’s degree (3 years) (AQF Level 7) or overseas equivalent with a minimum of 55% OR
  • Applicants must have completed a Graduate Certificate of IT (or similar cognate area) AQF level 8 equivalent OR
  • Applicants must have completed a Graduate Diploma of IT (or similar cognate area) AQF level 8 equivalent.

 

Assumed Knowledge

Students are assumed to have:

  1. basic numeracy skills of fundamental arithmetic – addition, subtraction, multiplication, and division;
  2. the ability to reason and to apply simple logical concepts; and
  3. the ability to apply, in context, a combination of different discipline-based knowledge and skills

 

English Language Requirements
Students applying for the Master of Information Technology (Data Science) must demonstrate English language proficiency through one of the following approved tests.

  • The minimum requirement is an IELTS Academic overall score of 6.5, with no individual band lower than 6.0. Equivalent scores are accepted from other tests:  
  • TOEFL iBT overall score of 81 with minimum scores of 18 in Reading, 22 in Writing, 18 in Listening, and 21 in Speaking;
  • PTE Academic overall score of 58, with no score lower than 53 in Reading, 62 in Writing, 48 in Listening, and 46 in Speaking;
  • Cambridge English C1 Advanced overall score of 176, with no band lower than 169;
  • CELPIP General overall score of 8, with no individual band lower than 7;
  • LanguageCert Academic overall score of 70, with no band lower than 65;
  • Michigan English Test (MET) overall score of 58, with no band lower than 53; and 
  • Occupational English Test (OET) overall score of 340, with minimum scores of 290.


Students who do not meet these requirements may be required to complete an ELICOS program, such as an English for Academic Purposes (EAP) course, with a recognised provider. For further details, please refer to the AIIHE Admission Policy and Procedures

Credit for Prior Learning
Conditions and procedures for application for Credit for Prior Learning are described in the AIIHE Credit for Prior Learning Policy and Procedures.

The structure of the Master of Information Technology (Data Science) course is informed by the professional recognition requirements of the Australian Computing Society (ACS), the Skills for the Information Age (SFIA) framework and the need for Information Technology graduates to have a broad-based education in the fundamental areas of information technology (Data Science) skills and knowledge. The course provides an information technology (Data Science)-based mandatory core subjects in years 1 and 2, four specialisation subjects and one Capstone Industry Project.

  • 15 subjects;
  • 10 credit points (cps) per subject; (Only MIT9317 provides 20 cps)
  • 160 credit points to complete the full course

Nationally accredited and registered with the Tertiary Education Quality and Standards Agency (TEQSA) under the code CRS1401856 and registered with the Commonwealth Register of Institutions and Courses for Overseas Students (CRICOS) under the code 120468K

Course Structure

Subject Code Subject Name CPS Pre-Req Co-Req
MIT8101
Introduction to Systems and Networks
10
None

None

MIT8102
Database Systems and Management
10
None

None

MIT8103
Programming Fundamentals
10
None

None

MIT8104
Professional Practice
10
None

None

MIT8201
Systems Analysis and Design
10
MIT8102

None

MIT8202
Fundamentals of Cybersecurity
10
MIT8101

None

MIT8203
Fundamentals of Data Science
10
MIT8103

None

MIT8204
Cloud Computing and Networks
10
MIT8101

MIT8203

Subject Code Subject Name CPS Pre-Req Co-Req
MIT9301
IT Project Management
10
MIT8201

None

MIT9303
Business Analytics
10
MIT8103

None

Specialisation

MIT9305
Statistical Methods for Data Science
10
None

None

MIT9307
Data Management, Modelling and Mining
10
MIT9305

None

Specialisation

MIT9309
Machine Learning and Optimisation Techniques
10
MIT8103
MIT8203

None

Specialisation

MIT9314
Advanced Cloud Data Analytics
10
MIT8204

None

Specialisation

MIT9317
Data Science Capstone Industry Project
20
MIT9301 and 100 credit pts
of which 80 credit pts must
be at level 8000

None