University of Sydney Handbooks - 2018 Archive

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Statistics is a major offered by the School of Mathematics and Statistics. Units of study in this major are available at standard and advanced level.

About the major

Statistics is pervasive in all areas of the sciences, the social sciences, finance and business, and is the key paradigm used to assess the strength of evidence from all kinds of data. In a statistics major, students learn about theoretical, computational, and applied statistics, and probability theory.

As part of the major students will apply the techniques that they learn to a variety of applications. Students learn about quantifying uncertainty, experimental design, probabilistic modelling and the latest techniques in statistical and machine learning. This major is essential training if you wish to become a professional statistician.

The 1000-level units of study cover a range of topics in mathematics and statistics and are offered at several levels, viz. Introductory, Fundamental, Regular, Advanced, and Special Studies, to suit various levels of previous knowledge. 2000-level, 3000-level and Honours (4XXX) units of study are mostly provided within one of the subject areas of applied mathematics, mathematical statistics and pure mathematics.

Advanced level units have more stringent prerequisites than regular units, and are significantly more demanding. Although the precise requirements vary from unit to unit, it is generally inadvisable for a student who has not achieved a Credit average in 2000-level mathematics to attempt an advanced 3000-level mathematics unit.

Various combinations of 1000-level units of study may be taken, subject to the prerequisites listed. Often specific 1000-level units of study are prerequisites for mathematics and statistics units at the 2000 and 3000-levels. Before deciding on a particular combination of 1000-level units of study, students are advised to check carefully the prerequisites relating to mathematics for all units of study.

The precise requirements for this major can be found in Table A. Alternatively, consult the school directly.

Requirements for completion

A major in Statistics requires 48 credit points, consisting of:

(i) 12 credit points of 1000-level units according to the following rules:

  • 6 credit points of calculus and 3 credit points of linear algebra and 3 credit points of statistics; or
  • 3 credit points of calculus and 3 credit points of linear algebra and 6 credit points of data science

(ii) 12 credit points of 2000-level core units
(iii) 12 credit points of 3000-level core units
(iv) 12 credit points of 3000-level selective units

A minor in Statistics is available and articulates to this major.

Pathway through the major

The requirements for a major in Statistics 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 Statistics major (over three years of a degree) is listed below.

Sample pathway: Statistics major (48 credit points)



Units of study


Semester 1 or 2

Core: Option A: (MATH1XX1 Applications of Calculus and MATH1XX3 Multivariable Calculus and Modelling and MATH1X02/1014 Linear Algebra and MATH1XX5 Statistical Thinking with Data)


Option B: ((MATH1XX1 Applications of Calculus or MATH1XX3 Multivariable Calculus and Modelling) and MATH1X02/1014 Linear Algebra and DATA1001 Foundations of Data Science).


Semester 1

Core: STAT2X11 Statistical Models

Semester 2

Core: DATA2002 Data Analytics: Learning from Data or STAT2912 Statistical Tests (Advanced)


Semester 1 or 2

Core: STAT3X22 Applied Linear Models

Selective: 3000-level units listed for major

Semester 1 or 2

Core: STAT3X23 Statistical Inference

Selective: 3000-level units listed for major


Please Note: This sample progression is meant as an example only. Depending on unit prerequisites, students may be able to complete these units in a different sequence from that displayed in the table above.

For details of the core and selective units of study required for the major or minor please refer to the Statistics section of the unit of study table, Table S, in this handbook.

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 Statistics: 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

First year enquiries email:

Other undergraduate enquiries email:
All inquiries phone: +61 2 9351 5804 or +61 2 9351 5787

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

Dr Michael Stewart
T +61 2 9351 5765
Learning Outcomes

Students who graduate from Statistics will be able to demonstrate:

  1. Effective communication of statistical concepts, methodology and results to a diverse range of audiences, from the general public to a scientifically educated audience.
  2. A deep understanding of the theoretical underpinnings of probability theory and statistics.
  3. The ability to identify and apply correct techniques to analyse data and to prepare data for analysis, when needed.
  4. A broad understanding of statistical principals for the design of experiments.
  5. An intuitive understanding of principals of decision making under uncertainty and a broad understanding of the types of questions that can be answered with a given set of data.
  6. The ability to use descriptive, interpretive and exploratory analysis of data by graphical and visualization tools as well as other methods.
  7. An understanding of statistical reasoning and inferential methods including the theory of maximum likelihood estimation, the framework of statistical hypothesis testing and common statistical procedures.
  8. The ability to formulate statistical questions in a disciplinary context, determine appropriate statistical modelling and model fitting, and understand the limitations of such approaches.
  9. Confidence with statistical computing and knowledge in using a range of statistical programming languages and computational resources.
  10. The ability to work in a team that includes both members with statistical expertise and members with expertise in other disciplines, but limited statistical expertise.