Master of Data Science (Applied) online
2.5 years duration part-time
$3,828 per course
Start or continue your journey towards a rewarding data science career with South Australia’s leading university for graduate employability.*
Applied = Real data, real impact
Strengthen your command of the most sophisticated data science tools through practical, real-world learning. All coursework is industry aligned - so you’ll be working with messy data sets and experiencing the same tools and database systems that top data scientists use today. Your hands-on expertise will be ready to take into the workplace immediately.
Studying with a university ranked in the top 1% worldwide guarantees an exceptional quality of learning, industry recognition and educators who are at the forefront of data-driven decision-making, machine learning and big data.
Skills that influence
Elevate your technical skills and become a versatile data scientist. Everything you will learn is tethered to the real world and designed for impact. Graduate with the ability to be truly influential no matter which industry or field you choose.
Marketable & industry aligned
The data science job market is growing as organisations employ large numbers of data specialists and data analysts to demystify today’s world. Be at the forefront of this exciting career opportunity, no matter what your background is.
"Data is the new oil, and now is the time to learn how to mine it." - Lewis Mitchell, Program Coordinator
Online degree structure
The online Master of Data Science (Applied) comprises fifteen courses, separated into three stages. There is the option to study the graduate certificate, the graduate diploma and the masters separately.
- Graduate Certificate in Data Science (Applied)
- Graduate Diploma in Data Science (Applied)
- Master of Data Science (Applied)
Your data science online learning experience
100% online learning, on your terms
Tap into your unique analytical and inquisitive mindset during interactive online lessons and self-paced activities. Go as fast as you want and study around your current employment or side projects.
Sharpen your focus
Upskill faster by completing one subject every six weeks, before enjoying a two-week break. This structure allows you to gain a new practical skill every six weeks so you can boost your performance at your current role or start your career move sooner.
Support when you need
You’ll have access to our expert enrolment advisors to help you navigate any challenges you face throughout your studies. Our advisors have a wealth of knowledge and expertise that will complement your learning style and support requirements.
What skills will you gain?
Through the Master of Data Science (Applied), you will experience:
- extracting meaningful information by manipulating real-world, messy datasets with Python and R
- using best-in-class tools, techniques and theories
- fail-safe communication tactics
- the strategic impact of data science within various industry contexts
- technical data practice and management
- how to follow ethics, codes of conduct and other standards.
A University of Adelaide 100% online Master of Data Science (Applied) qualification is the first step towards a new, dynamic career.
Data Science skills are transferable to virtually any industry: big companies, the public sector and innovative startups are all offering data scientists competitive salaries to help them unlock the way forward.
Perhaps you want to use data to improve operational performance or to uncover new exciting opportunities. The choice is yours. Course content covers an exciting array of data from multiple sectors.
*QS Graduate Employability Ranking 2020.
Human and ethical factors in computer science
COMP SCI 7212OL
You will study two important areas in contemporary computing – human factors and ethical theory and practice – to determine whether your work aligns with definitions of what is right for individuals, companies, and society. By combining two areas of data science, participants will learn how to apply tools and analysis tactics to establish whether the computing tasks they are being asked to perform are fit for purpose in terms of both usage and ethics.
Data taming, modelling and visualisation
A practical introduction to finding relationships in data using statistical methods, this course covers the principles of taming and tidying data. It familiarises students with:
- different types of data
- exploratory data analysis
- visualisation techniques
- data transformation
- model fitting and interpretation.
One of the course’s core aims is to introduce R programming for data science applications through real-world case studies.
Foundations of computer sciences: Python A*
COMP SCI 7210OL
This course will develop your coding and problem-solving skills with a focus on data science principles. You will learn algorithm design and programming concepts such as:
- data selection iteration
- functional decomposition
- data abstraction and organisation.
You will complete this course with the confidence to apply fundamental software development skills including Python programming language, debugging, testing and other fundamentals of programming practice.
*Students entering from a cognate area who have already completed Python A will be required to undertake appropriate alternative courses. The alternative course/s may come from other discipline areas.
Applied data science
An introduction to the role and application of data science in modern organisations and societies, this course includes processes for data collection, analysis, verification and validation. Case studies will be used to demonstrate current best practice and common pitfalls.
Foundations of computer sciences: Python B*
COMP SCI 7211OL
This course introduces fundamental concepts of data science applications in Python. You will develop several applied data science skills, including:
- object-oriented fundamentals, methods, and classes
- algorithms and problem-solving processes and strategies
- computational complexity of algorithms
- software development tools and techniques.
*Students entering from a cognate area who have already completed Python B will be required to undertake appropriate alternative courses. The alternative course/s may come from other discipline areas.
Applications of data science
This course provides a practical introduction to data modelling, analysis and prediction using contemporary software packages. You will also be given an overview of common data science techniques and their implementation in software libraries.
Mathematical Foundations of Data Science
You will learn fundamental mathematical concepts relevant to computer science in this course.
It also provides a basis for further postgraduate study in the following fields:
- data science
- statistical machine learning
- cyber security.
Real data: Modern methods of finding hidden patterns
Building on skills acquired in the DATA 7201OL Data taming, modelling and visualisation course, this course introduces advanced techniques for extracting meaningful information from real-world datasets. It covers a range of methods, including:
- generalised linear models
- advanced regression techniques
- unsupervised statistical learning.
This course will also teach advanced R programming techniques for data science, as well as data-wrangling techniques.
Big data analysis and industry project
COMP SCI 7319OL
Practice evaluating, selecting and applying relevant data science techniques, principles and theory to a medium-scale, real-world, industry-motivated dataset. You will need to identify any social concerns and apply an appropriate ethical frameworks for data management. You will then present your findings via both a written and oral presentation that covers your project design, plan, methodologies, and outcomes.
Using machine learning tools
COMP SCI 7137OL
Learn to build software that uses pre-existing tool kits as appropriate to solve a variety of machine learning problems. The course will have a practical focus using case studies and work place examples, with an emphasis on ensuring that solutions are valid and verifiable.
Working with big data
Use big data tools to explore large data sets. Discover practical algorithms used for solving problems when mining of massive datasets. It focuses on parallel algorithmic techniques that are used for large datasets in the area of cloud computing. Furthermore, stream processing algorithms for data streams that arrive constantly, page ranking algorithms for web search, and online advertisement systems are studied in detail.
Business data and cyber security
Business data & cyber security will prepare you for navigating the constantly changing use of data and information in a business world that requires ongoing cyber security awareness and vigilance. Value and vulnerability of business data for decision making and problem solving are a core focus. You will emerge from this course with the skills required to apply cyber secure practices in a business setting.
APP MTH 7201OL
This course is focused on equipping students with simulation techniques to underpin decision-making. Simulation is widely used to model systems, to evaluate risk, and to optimise objective functions, with the goal to inform decisions. Building up from uniform random generation, some of the key simulation techniques used for efficient simulation to support decision-making will be presented. This course covers:
- uniform random number and random variable generation
- random process generation
- discrete-event simulation
- basic statistical analysis of simulation data
- applications in systems
- modelling and risk analysis
COMP SCI 7415OL
This course will prepare you for advanced research by examining how to plan, conduct and report on data driven investigations. Techniques for each of these steps will be covered including:
- formulating research questions
- framework building
- data analysis (using both qualitative and quantitative methods)
- building evidence
- assessing validity
- reporting and disseminating research outcomes.
Data science research project*
Undertake an individual applied research project based within a workplace or industry context. This can form part of your portfolio you use for future job applications.
*The final research project is the equivalent to two courses and will span two online teaching periods.
Master of Data Science (Applied)
Academic entry requirements
To be eligible for the 100% online Master of Data Science (Applied) and the Graduate Diploma in Data Science (Applied) you will need to meet both of the following entry requirements:
1. A completed bachelor degree or equivalent in any discipline with a minimum GPA of 4.5 (7.0 GPA scale).
2. Successfully completed SACE Stage 2 Mathematical Methods (or equivalent) during Senior Secondary School.
It’s important to have a solid grasp of core mathematical concepts before undertaking a data science program as almost all of the techniques are underpinned by maths. If you didn’t complete your higher education in South Australia, refer to the table to see the equivalent programs per state.
What if I do not meet the maths entry requirement?
If you haven’t completed SACE Stage 2 Maths Methods, you can enrol in the Graduate Certificate in Data Science (Applied) and MathTrackX. MathTrackX covers the mathematics you’ll need to continue onto the Master of Data Science (Applied). Learn more about alternative entry into the Master of Data Science (Applied).
English language requirements
In order to study with the University of Adelaide, you must be proficient in speaking, listening, reading and writing in English as this is the language of instruction.
You must be able to demonstrate that you meet the minimum English language requirements. However you will not be required to provide evidence of English language proficiency if you are an Australian citizen, Australian Permanent Resident (visa status) or hold a passport from one of the following countries: Canada (English speaking provinces only), New Zealand, the Republic of Ireland, the United Kingdom and the United States.
If English is not your first language of instruction, further documentary evidence will be required so please download the Minimum English Language Requirements document (Postgraduate Coursework Programs section) for information about acceptable English language tests.
How to apply
It is possible to enrol in either the graduate certificate, graduate diploma or masters program. If you wish to apply for the graduate diploma or masters, you will need to provide proof that you have completed the required level of mathematics.
Complete application form and upload supporting documents which include:
- valid photo ID (passport or driver's licence)
- proof of citizenship / permanent residency
- transcripts from your complete bachelor's degree or equivalent if applicable
- proof of completion of Mathematical Methods or equivalent
- change of name documents if applicable (e.g marriage certificate)
- English language requirements (if you are an international applicant).
You’ll be hearing from our enrolment team in the coming weeks who will confirm the outcome of your application.
Graduate Certificate in Data Science (Applied)
To be eligible for the 100% online Graduate Certificate in Data Science (Applied), you will need to meet the following requirement:
- A completed Bachelor degree or equivalent in any discipline with a minimum GPA of 4.5 (7.0 GPA scale).
What is the total cost?
$61,248 or $3,828 per course (2021 pricing). Please note that the data science research project is two courses.
Is there FEE-HELP available?
Yes, Australian citizens are eligible for a HELP loan. You must also be enrolled in a program with the University of Adelaide by the enrolment deadline (census), and have not reached the HELP loan limit.
Once you have been offered a place in a University of Adelaide program, you will be able to apply for a HELP loan as a part of the enrolment process. Please see our FAQs section for further details.