Tuition Fee
Not Available
Start Date
Not Available
Medium of studying
Not Available
Duration
Not Available
Details
Program Details
Degree
Masters
Major
Artificial Intelligence | Data Analysis | Data Science
Area of study
Information and Communication Technologies | Natural Science
Course Language
English
About Program
Program Overview
Master of Data Science
The Master of Data Science is a postgraduate degree that requires the completion of 72 credit points. The program is designed to provide students with a comprehensive understanding of data science principles, practices, and technologies.
Program Structure
To qualify for the award of the Master of Data Science, a candidate must complete the following:
- 30 credit points of Core units of study, consisting of:
- 18 credit points of Data Science Core units of study
- 12 credit points of Professional Core units of study
- 12 credit points of Capstone Project units of study, taken either as:
- Two 6 credit point units, DATA5707 and DATA5708, over two semesters
- A 12 credit point unit, DATA5703 or DATA5709, in one semester
- A minimum of 18 credit points of Specialisation units of study or Data Science Specialist units of study
- A maximum of 12 credit points of Elective or Foundation units of study
Research Pathway
For the Research Pathway, students must complete:
- 30 credit points of Core units of study
- 24 credit points of Research Pathway units of study
- 18 credit points of Specialisation units of study or Data Science Specialist units of study
- No credit points from the Foundation or Elective units of study
Graduate Diploma in Data Science
To qualify for the award of the Graduate Diploma in Data Science, a candidate must complete 48 credit points of units of study, including:
- A minimum of 12 credit points of Data Science Core units of study
- A minimum of 6 credit points of Professional Core units of study
- A minimum of 12 credit points of Data Science Specialist units of study
- A maximum of 12 credit points of Foundation or Elective units of study
Graduate Certificate in Data Science
To qualify for the award of the Graduate Certificate in Data Science, candidates must complete 24 credit points of units of study, including:
- 12 credit points of Data Science Core units of study, consisting of COMP5310 and STAT5003
- 12 credit points of Data Science Specialist units of study
Unit of Study Table
The following units of study are available:
Core Units of Study
Data Science Core Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| COMP5048: Visual Analytics | 6 | Experience with data structures and algorithms |
| COMP5310: Principles of Data Science | 6 | Good understanding of relational data model and database technologies |
| STAT5003: Computational Statistical Methods | 6 | STAT5002 or equivalent introductory statistics course |
Professional Core Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| INFO5990: Professional Practice in IT | 6 | Students enrolled in INFO5990 are assumed to have previously completed a Bachelor's degree in some area of IT |
| INFO5995: Introduction to Cybersecurity | 6 | No prerequisites |
Data Science Specialist Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| COMP5046: Natural Language Processing | 6 | Knowledge of an OO programming language |
| COMP5318: Machine Learning and Data Mining | 6 | Experience with programming and data structures |
| COMP5328: Advanced Machine Learning | 6 | COMP5318 or COMP4318 or COMP3308 or COMP3608 |
| COMP5329: Deep Learning | 6 | COMP4318 or COMP5318 |
Foundation Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| COMP9001: Introduction to Programming | 6 | No prerequisites |
| COMP9017: Systems Programming | 6 | COMP9003; discrete mathematics and probability |
| COMP9110: System Analysis and Modelling | 6 | Experience with a data model |
Elective Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| COMP5047: Pervasive Computing | 6 | ELEC1601 and (COMP2129 or COMP2017 or COMP9017) |
| COMP5216: Mobile Computing | 6 | COMP5214 or COMP9103 or COMP9003 |
| COMP5313: Large Scale Networks | 6 | Algorithmic skills gained through units such as COMP2123 or COMP2823 |
Capstone Project Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| DATA5703: Data Science Capstone Project | 12 | A candidate of Master of Data Science who has completed 24 credit points from Data Science Core or Data Science Elective units of study |
| DATA5707: Data Science Capstone A | 6 | A part-time candidate of Master of Data Science who has completed 24 credit points from Data Science Core or Data Science Elective units of study |
| DATA5708: Data Science Capstone B | 6 | A part-time candidate of Master of Data Science who has completed 24 credit points from Data Science Core or Data Science Elective units of study |
Research Pathway Units of Study
| Unit of Study | Credit Points | Assumed Knowledge/Prerequisites |
|---|---|---|
| DATA5702: Data Science Research Project A | 12 | 12 credit points of Data Science Core and 12 credit points of Specialisation Core or Data Science Specialist units of study with a WAM of 75 or above |
| DATA5704: Data Science Research Project B | 6 | 12 credit points of Data Science Core and 12 credit points of Specialisation Core or Data Science Specialist units of study with a WAM of 75 or above |
Specialisations
The Master of Data Science offers the following specialisations:
Data Engineering Specialisation
- COMP5338: Advanced Data Models
- COMP5339: Data Engineering
- COMP5349: Cloud Computing
Machine Learning Specialisation
- COMP5318: Machine Learning and Data Mining
- COMP5328: Advanced Machine Learning
- COMP5329: Deep Learning
Unspecified Specialisation
- 18 credit points from the Data Science Specialist units of study table
See More