Data Science

Study in the discipline of Data Science is jointly offered by the School of Mathematics and Statistics in the Faculty of Science and the School of Computer Science in the Faculty of Engineering. Units of study in this major are available at standard and advanced level.

About the major

Data is an essential asset in many organisations as it enables informed decision making into many areas including market intelligence and science. In the major in Data Science, you will learn computational and analytical skill sets that stem from statistics and computer science, to manage, interpret, understand, analyse and derive key knowledge from the data.

You will develop critical thinking about data and its use, a deep understanding of the core technical skills required and an appreciation for the context in which that data was collected. At the 3000-level of study and beyond, you will develop the ability to understand problems from many disciplines and place a data-driven problem into an analytical framework, solve the problem through computational means, interpret the results and communicate them to clients or collaborators.

Requirements for completion

The Data Science major and minor requirements are listed in the Data Science unit of study table.

Contact and further information

sydney.edu.au/science/schools/school-of-mathematics-and-statistics

First year enquiries:


Other undergraduate enquiries:


All enquiries: +61 2 9351 5787

Major coordinator
Professor Jean Yang

Learning Outcomes

Students who graduate from Data Science will be able to:

1. Exhibit a broad and coherent body of knowledge in data science, and be able to describe the relationships between context-specific knowledge and data and evaluating how these can guide data analytics.
2. Exhibit deep knowledge of the underlying concepts and principles of experimental design, analysis and data outputs, of the relationships between these concepts, and of potential pitfalls.
3. Use quantitative models or visualisation methods on multiple types of data.
4. Manage data, metadata and derived knowledge, using appropriate storage, access and administration tools.
5. Communicate concepts and findings in data science through a range of modes for a variety of purposes and audiences, using evidence-based arguments that are robust to critique.
6. Identify analytical approaches appropriate to a specific problem in data analysis, simulation-based modelling or equation-based modelling.
7. Create and use databases and graphical information systems using programming skills.
8. Address authentic problems in data science, working professionally and ethically and with consideration of cross-cultural perspectives, within collaborative, interdisciplinary teams.