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Graduate Diploma in Data Science (Applied)

Duration

1.4 years (Part­Time)

SATAC Code

GDDSA

Program Overview

Data is everywhere, and understanding it is a key skill which could set you apart in our changing world. Learn how to code, handle data and manage visualisation plus develop a vast range of problem solving skills.

What could your future look like?

Upon completion, our 100% online program could enable a future career in a technology start-­up, a government organisation or financial institution. Data scientists are in high demand across a wide range of industries—whether you want to work in finance, information technology, healthcare or many other industries.

For more information, watch the interactive information session where Dr Lewis Mitchell provides an introduction to Data Science and an overview of the program. 

What will you learn?

Our Graduate Diploma in Data Science (Applied) will allow students to:

  • Apply, evaluate and use the principles of data science within a real-world context, including the specific requirements of large-scale data analysis, in an area of specialisation.
  • Demonstrate knowledge and understanding of the technical practice, management and strategic impact of data science, and its application, within industry contexts.
  • Apply, evaluate and use best-practice tools, techniques and theory of data science within a range of application domains.
  • Adopt and employ professional attitudes, standards and values.
  • Use highly effective interpersonal skills to enable empathetic and effective communication with a range of audiences.

  • Human and ethical factors in data science - COMPSCI 7212OL

    In this course, you will be introduced to two important areas in contemporary computing. You will study those areas that discuss whether the tasks that we seek to achieve fit our definitions of what is right for individuals, companies, and our society. By combining these two areas of study, participants will be introduced to tools, thinking, and analyses 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 - DATA 7201OL

    A practical introduction to finding relationships in data using statistical methods. The course introduces the principles of taming and tidying data, types of data, exploratory data analysis and visualisation, data transformation, as well as model fitting and interpretation. A focus will be to introduce R programming for data science applications, particularly through real-world case studies.

  • Foundations of computer science: Python A - COMPSCI 7210OL

    This course will develop your coding and problem-solving skills with a focus on data and data science. You will learn algorithm design as well as fundamental programming concepts such as data, selection, iteration and functional decomposition, data abstraction and organisation. You will build fundamental software development skills including the use of the Python programming language and tools, debugging, testing and fundamentals of good programming practice, style and design.

  • Applied data science - DATA 7202OL

    An introduction to the role and application of data science in modern organisations and society, including processes for data collection, analysis, verification and validation. Case studies will be used to demonstrate current best practice as well as common pitfalls.

  • Foundations of computer science: Python B - COMPSCI 7211OL

    This course introduces fundamental concepts of building data science applications in Python. You will cover object oriented fundamentals, methods, and classes, algorithms and problem solving processes and strategies, computational complexity of algorithms, as well as software development tools and techniques.

  • Applications of data science - DATA 7301OL

    This course provides a practical introduction to data modelling, analysis and prediction using contemporary software packages, including a selection of tools and techniques that are appropriate for different types and scales of data. You will get an overview of common techniques and their implementation in software libraries.

  • Mathematical foundations of data science - MATHS 7212OL

    This course introduces fundamental mathematical concepts relevant to computer science and provides a basis for further postgraduate study in data science, statistical machine learning, and cyber security.

  • Real data: Modern methods for finding hidden patterns - DATA 7302OL

    This course builds upon DATA7201 Data Taming, modelling and visualisation, to introduce advanced modern techniques for extracting meaningful information from real-world, messy datasets. The course covers methods such as generalised linear models, classification, advanced regression techniques, and unsupervised statistical learning. A particular focus will be data wrangling techniques for non-standard, processing, networks, and longitudinal data. The course will also teach advanced R programming techniques for data science.

The Graduate Diploma in Data Science is designed for established working professionals who have recognised the opportunity that data science skills present in pivoting their career trajectory. It is the ideal qualification that enables students to leverage their current skills and expertise in complementary roles such as business analytics, program management or technology, with newfound skills in data science.

  • Data science is a rapidly growing industry need. With expertise needed both in technical data science, and in the application of data science principles, across a number of domains.

  • Data science roles have grown over 650% since 2012, with 2.7 million new jobs forecasted globally by 2020 and 11.5 million jobs expected by 2026.

To be eligible for the Graduate Diploma in Data Science (Applied), you will need an undergraduate Bachelor's degree or equivalent in any discipline with a minimum GPA of 4.5. 

You will also need to have complete SACE Stage 2 Mathematical Methods or equivalent.