Master of Data Science (MDataSc)
Master of Data Science (MDataSc)
Program Code
MDSCI
Program Faculty
Faculty of Sciences, Engineering and Technology
Academic Year
2025
These Program Rules should be read in conjunction with the University's policies (https://www.adelaide.edu.au/policies).
Overview
The Master of Data Science is a program designed for students who wish to develop new skills in the growing area of data science, with a specific focus on large-scale data analysis, or big data. In this program, students undertake a variety of core and elective courses, designed to combine skills in computer science, mathematics, data science and data science analysis, as well as a significant project designed to combined skills developed across the program.
The program provides the necessary skills for entering the world of Big Data and Data Science, an emerging area of necessity for many fields, including Science, Engineering, Economics and Digital Humanities. The program will help learners understand how data is changing our world, and how learners can apply data science techniques to drive changes in their organisation or area. The program will provide an introduction to emerging field of data, and introduce fundamental analysis techniques, explored through a range of case studies, gaining deeper knowledge of relevant computational and mathematical techniques, such as machine learning and data mining.
The Master of Data Science is an AQF Level 9 Qualification with a standard full-time duration of 2 years.
Program Learning Outcomes
- Demonstrate knowledge and understanding of the content, technologies and practices of data science, especially in relation to challenging data, and how it can apply to informing organisational change.
- Apply, evaluate and use best-practice algorithms, techniques and theory of machine learning, statistics, optimisation, and distributed computing to the analysis and transformation of data in a range of application domains.
- Demonstrate understanding of the social and ethical aspects of data science, to inform the design and development of data science applications within a sound ethical framework.
- Apply principles of data analysis, presentation and visualisation to effectively communicate patterns and relationships in data.
- Demonstrate strong independent and collaborative problem-solving skills to apply data science techniques to aid decision-making in real-world problems
Conditions
Interruption of program: Students must apply for permission from the Executive Dean or delegate before taking a Leave of Absence. Any extension of the leave without approval will result in the loss of place in the program but an application may be made to be re-admitted to the program subject to the admission procedures in place at the time.
Program Maximum Duration: As specified in Table 1 of the Coursework Academic Programs Policy, this program must be completed within a Maximum Duration which includes any periods of non-enrolment, leave of absence or approved study at other institutions for credit towards a Program.
Academic Program Rules for Master of Data Science
There shall be a Master of Data Science.
Qualification Requirements
To qualify for the degree of Master of Data Science, the student must complete satisfactorily a program of study consisting of the following requirements with a combined total of not less than 48 units comprising:
- Core courses to the value of 24 units
- A research project to the value of 12 units
- Elective courses to the value of 12 units
Core Courses
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Core Courses
To satisfy the requirements for Core Courses students must complete courses to the value of 24 units.
All of the following courses must be completed:
Subject/Catalogue Course Title Unit Value COMP SCI 7210 Foundations of Computer Science A 3 COMP SCI 7211 Foundations of Computer Science B 3 COMP SCI 7212 Human and Ethical Factors in Computer Science 3 COMP SCI 7328 Concepts in Cyber Security 3 MATHS 7027 Mathematical Foundations of Data Science 3 MATHS 7107 Data Taming 3 PROJMGNT 5021 Project Management Fundamentals 3 STATS 7022 Data Science PG 3 -
Research Project
To satisfy the requirements for Research Project students must complete courses to the value of 12 units.
All of the following courses must be completed:
Subject/Catalogue Course Title Unit Value MATHS 7097A Data Science Research Project Part A 0 MATHS 7097B Data Science Research Project Part B 12
Electives
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Electives
To satisfy the requirements for Electives students must complete courses to the value of 12 units.
Courses to the value of 9 units from the following:
Subject/Catalogue Course Title Unit Value APP MTH 7124 Decision Science PG 3 COMP SCI 7306 Mining Big Data 3 COMP SCI 7314 Statistical Machine Learning 3 COMP SCI 7318 Deep Learning Fundamentals 3 COMP SCI 7416 Applied Machine Learning 3 STEM 7555 STEM Internship 3 and
Courses to the value of 3 units from the following:
Subject/Catalogue Course Title Unit Value COMP SCI 7007 Specialised Programming 3 COMP SCI 7059 Artificial Intelligence 3 COMP SCI 7076 Distributed Systems 3 COMP SCI 7088 Systems Programming 3 COMP SCI 7201 Algorithm & Data Structure Analysis 3 COMP SCI 7209 Big Data Analysis and Project 3 COMP SCI 7305 Parallel and Distributed Computing 3 COMP SCI 7317 Using Machine Learning Tools PG 3 COMP SCI 7407 Advanced Algorithms 3 COMP SCI 7416 Applied Machine Learning 3 COMP SCI 7417 Applied Natural Language Processing 3 MATHS 7103 Probability & Statistics PG 3 MATHS 7105 Data Literacy 3 PHIL 7005 Machine Learning and Artificial Intelligence 3 STATS 7054 Statistical Modelling 3 STATS 7107 Statistical Modelling and Inference 3 or
With the approval of the Program Director, courses to the value of 6 units from the following:
Subject/Catalogue Course Title Unit Value APP MTH 7044 Applied Mathematics Topic C 3 APP MTH 7045 Applied Mathematics Topic B 3 APP MTH 7048 Applied Mathematics Topic A 3 APP MTH 7049 Applied Mathematics Topic D 3 APP MTH 7087 Applied Mathematics Topic E 3 APP MTH 7088 Applied Mathematics Topic F 3 MATHS 7026 Cryptography PG 3 STATS 7004 Statistics Topic A 3 STATS 7008 Statistics Topic D 3