Flinders University
Graduate Diploma of Data Science
- Delivery: Face to Face
- Study Level: Postgraduate
- Duration: 12 months
- Course Type: Graduate Diploma
Explore different techniques for data analysis, including computational methods, statistical techniques, data mining and knowledge discovery.
Course overview
The Graduate Diploma in Data Science covers the fundamental skills and techniques used in data science. It allows students to explore common application domains, such as health, business, science and technology. The course provides the skills needed to interpret data using current methods and create the tools needed to solve future problems. This includes understanding coding, statistical methods, machine learning and data mining.
The course articulates with the 18-unit Graduate Certificate in Data Analytics and the 72-unit Master of Data Science, and the sequentially developed topics enable progression through the three awards.
Key facts
What you will study
To complete the Graduate Diploma of Data Science, you must complete 36 units.
Core Units
Select eight topics from the following:
- Data Engineering GE
- Database Modelling and Information Management GE
- Neural Networks and Machine Learning GE
- Fundamentals of Artificial Intelligence GE
- ICT Management and Professional Standards
- Engineering Programming GE
- Probability GE
- Data Science GE
- Advanced Professional Skills
Entry requirements
Admission requirements
Applicants who do not hold the Graduate Certificate in Data Analytics must normally hold a degree or equivalent qualification from an approved tertiary institution in science, medical science, information technology, mathematics, statistics or a closely related discipline.
However, the Dean (Education) may, under certain circumstances and subject to specific conditions, admit others who can show evidence of fitness for candidature.
English Language requirements
International Student English Language Requirements
Outcomes
Learning outcomes
- Apply high-level cognitive, technical and creative skills in processing, analysing, synthesising and interpreting large and complex datasets using state-of-the-art concepts, theories, tools and techniques; identify patterns, trends and insights from data and make data-driven decisions to solve complex real-world problems.
- Create and effectively apply machine learning algorithms and techniques to develop predictive models and solutions using programming languages commonly used in data science, such as Python, R and SQL, and the relevant libraries and frameworks.
- Adhere to best practices for responsible data use and handling, including recognising potential biases involved in working with data and applying skills and knowledge in a professionally responsible manner with a high level of personal autonomy and accountability.
- Effectively communicate complex technical concepts and findings to both technical and non-technical audiences verbally, in writing, and by creating appropriate visualisations of data.
- Demonstrate knowledge of the research principles and methods applicable to Data Science, including working professionally as an individual and in a team.
Fees and FEE-HELP
Annual indicative fee in 2025: $33,030 (domestic full-fee paying place)
The annual indicative fee is based on a full-time study load of 36 units.
A student’s annual fee may vary in accordance with:
- The number of units studied per term.
- The choice of major or specialisation.
- Choice of units.
- Credit from previous study or work experience.
- Eligibility for government-funded loans.
Student fees shown are subject to change. Contact the university directly to confirm.
FEE-HELP loans are available to assist eligible full-fee paying domestic students with the cost of a university course.