Master of Data Science

Degree Level Postgraduate

Years

International students

Start date
February
July

Campus
Mawson Lakes

Duration
2 year(s) full-time

Mode
On-campus/Online

Program Code
LMDS

Fees
AUD$ 40,900 per annum (per 1.0 EFTSL) for students enrolled in 2025

English Language Requirements

IELTS total [6.5]
IELTS reading [6.0]
IELTS writing [6.0]

Equivalent English qualifications (PDF)

International Admission by Country
See full entry requirements

CRICOS Code
079912G

Entry requirements

Applicants to the Master of Data Science will normally have:
- a Bachelor degree or equivalent from a recognised higher education institution with a minimum of one year of full-time study in Mathematics or Information Technology or Data Science or a combination thereof; OR
- a Graduate Certificate or Graduate Diploma in Data Science or equivalent from a recognised higher education institution.
English language entry requirements
In addition to meeting academic entry requirements, international students who speak English as a second or additional language must also meet the University's English language entry requirements. The minimum language requirements for this program are:
English language test
- IELTS total [6.5]
- IELTS reading [6.0]
- IELTS writing [6.0]
Please access the following link for a comprehensive list of English language tests accepted by UniSA and other important information in relation to meeting the University’s language requirements: English Language Equivalence Table (PDF)
View full entry requirements Hide full entry requirements

Aim

This program provides graduates with a coherent and in-depth knowledge of current techniques and research in data science, with a theme of applying this knowledge and research in the context of professional practice.

Content & Structure

This program consists of 72 units of study. The first semester enables students who enter with either a mathematics or information technology background to develop, through providing alternative pathways for each, a set of foundation skills on which the remainder of the program is built. The first semester includes professional skills for an IT business professional and in third semester a best practice course for professional development. The next two semesters each consist of two core data analytics courses and a 9 unit course where students apply the theory and techniques to realistic problems in a professional development context. The final semester consists of two advanced data analytics courses and a 9 unit capstone project.

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What courses you'll study

Course name	Area and cat no.	Units	Reference
Semester 1
Big Data Concepts	INFS 4020	4.5
Problem Solving in the Digital Age	COMP 5076	4.5
Business Practices for IT Professionals	INFS 5058	4.5
Statistics for Data Science	MATH 4044	4.5
Semester 2
Statistical Programming for Data Science	COMP 5070	4.5
Data Wrangling and Social Media Analytics	INFS 5144	4.5
Data Visualisation	INFS 5116	4.5
Directed Elective		4.5	Note(s): 1
Semester 3
Unsupervised Methods in Analytics	INFS 5102	4.5
Predictive Analytics	INFS 5100	4.5
Advanced Databases, Warehouses and The Cloud	INFS 5143	4.5
Masters Research Theory and Practice	ENGG 5020	4.5
Semester 4
Customer Analytics in Large Organisations	INFS 5096	4.5
Advanced Analytic Techniques	MATH 5045	4.5
Capstone Professional Project	INFT 5021	9

Electives

Course name	Area and cat no.	Units
Directed Electives
Probabilities and Data	MATH 4043	4.5
Security Principles	INFS 5115	4.5
Analytics for Decision Making	INFS 3075	4.5
Risk Management and Governance	INFS 3083	4.5
Advanced Business Intelligence and Analytics	INFS 5135	4.5

Rules & notes

Rules

1. Students entering this program with the Graduate Certificate in Data Science will be given credit for the first semester of the program.

2. Students entering this program with the Graduate Diploma in Data Science will be given credit for the first year of the program.

Notes

1. Students must choose a course from the Directed Elective groups, in consultation with and approval of the Program Director. The chosen electives should complement current knowledge and experience.