University of Sydney Handbooks - 2018 Archive

Download full 2018 archive Page archived at: Fri, 21 Sep 2018 05:39:46 +0000

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 Information Technologies in the Faculty of Engineering and Information Technologies. 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

A major in Data Science requires 48 credit points, consisting of:

(i) 6 credit points of 1000-level core units
(ii) 6 credit points of 1000-level units according to one of the following rules:

  • 6 credit points of selective units, or
  • 3 credit points of statistics units and 3 credit points of computations units, or
  • 3 credit points of advanced statistics and 3 credit points of calculus and linear algebra units

(iii) 12 credit points of 2000-level core units
(iv) 6 credit points of 2000-level selective units
(v) 6 credit points of 3000-level interdisciplinary project units
(vi) 6 credit points of 3000-level methodology-focussed units
(vii) 6 credit points of 3000-level methodology or application and discipline-focussed units

A minor in Data Science is available and articulates to this major.

Pathway through the major

The requirements for a major/minor in Data Science are spread out over three years of the degree (possibly four years if students are completing a combined Bachelor of Advanced Studies degree).

A sample pathway for the Data Science major (over three years of a degree) is listed below.

Sample pathway: Data Science major (48 credit points)



Units of study


Semester 1

Core: DATA1001 – Foundation of Data Science

Semester 2

Core: DATA1002 – Informatics: Data and Computation


Semester 1

Core: DATA2001 – Data Science: Scale and Data Diversity

Semester 2

Core: DATA2002 – Data Analytics: Learning from Data


Semester 1 or 2

Core: DATA3001 – Interdisciplinary Data Science Project

Semester 1 or 2

Selective: 3000-level units listed for major

Fourth year

The fourth year is only offered within the combined Bachelor of Science/Bachelor of Advanced Studies course.

Advanced coursework
The Bachelor of Advanced Studies advanced coursework option consists of 48 credit points, which must include a minimum of 24 credit points in a single subject area at 4000-level, including a project unit of study worth at least 12 credit points. Space is provided for 12 credit points towards the second major (if not already completed). 24 credit points of advanced study will be included in the table for 2020.

Requirements for Honours in the area of Data Science: completion of 24 credit points of project work and 24 credit points of coursework.

Honours units of study will be available in 2020.

Contact and further information


All enquiries phone: +61 2 9351 5804 or +61 2 9351 5787

School of Mathematics and Statistics
Level 5, Carslaw Building F07
University of Sydney NSW 2006

Professor Jean Yang
T +61 2 9351 3012
Learning Outcomes

Students who graduate from Data Science will be able to demonstrate:

Interdisciplinary Skills

  1. Ability to engage with problems from many diverse areas of application and to understand the relationships between a given problem and data collected to solve the problem.
  2. Ability to relate context specific knowledge to data, to understand how data can be used to generate context specific knowledge, and know how this knowledge can guide data analytics.

Foundational Understanding

  1. Understanding of the importance of experimental design, its relationship with data output, and how this data should be analysed and evaluated, including potential pitfalls.
  2. Ability to identify, at a general level, the type of data analytical approach required for a particular problem; whether that is data analysis, simulation based modelling or equation-based modelling.
  3. Understanding of how the data context, organizational constraints and quality issues have implications for flow-on impacts in further stages of the analysis.

Data Science Methods and Tools

  1. Skills in data management with an understanding of how data, metadata, and derived knowledge (including analytical models) are stored, accessed, and administered.
  2. A range of computational skills including programming, choosing scientific data formats, creating and using databases (for storing and accessing metadata) and use of graphical information systems (for mapping and sharing high dimensional data). These skills also include understanding the principles of programming and the ability to translate this knowledge to new computational code and to create tools.
  3. Data analytical competencies that include, but are not limited to, the use appropriate of quantitative models or visualisation methods on multiple data types to:
  • enable prediction of outcome,
  • recognise significant patterns and trends,
  • critically assess the strengths and weaknesses of different analytical approaches.

Communication Skills

  1. Ability and experience to confidently use one’s data analytical competency to communicate discipline-specific outcomes in written and verbal form, and for decision making.

Problem Awareness

  1. An awareness of data integrity issues including appreciation of data privacy and ethical issues.
  2. General understanding of how data analytical tools can be automated and implemented efficiently and up-scaled if necessary using the available technologies.