University of Sydney Handbooks - 2020 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, data science, financial mathematics and statistics, pure mathematics and statistics.

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 and statistics 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:
(a) 6 credit points of calculus and 3 credit points of linear algebra and 3 credit points of statistics*. (Students in the Mathematical Sciences program must choose this option^);
(b) 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) 6 credit points of 3000-level interdisciplinary project units
(v) 6 credit points of 3000-level or 4000-level selective units
*Students not enrolled in the Bachelor of Science may substitute ECMT1010 Introduction to Economic Statistics or BUSS1020 Quantitative Business Analysis
^If elective space allows, students may substitute DATA1001/1901 Foundations of Data Science for the statistics unit

A minor in Statistics requires 36 credit points, consisting of:

(i) 12 credit points of 1000-level units according to the following rules:
(a) 6 credit points of calculus and 3 credit points of linear algebra and 3 credit points of statistics; or
(b) 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 or 4000-level selective units

First year

Option A: (MATH1XX1 and MATH1XX3 Multivariable Calculus and Modelling and MATH1X02/1014 Linear Algebra and MATH1XX5 Statistical Thinking with Data)
Option B: ((MATH1XX1 or MATH1XX3 Multivariable Calculus and Modelling) and MATH1X02/1014 Linear Algebra and DATA1001).

Second year

Core: STAT2X11 Probability and Estimation Theory and DATA2X02 Data Analytics: Learning from Data

Third year

STAT3X22 Applied Linear Models, STAT3X23 Statistical Inference
and 12 credit points from a selection of:
STAT3012 Applied Linear Models, STAT3888 Statistical Machine Learning, STAT4025 Time Series and STAT4026 Statistical Consulting.

In your third year, you must take at least one designated project unit.

Fourth year

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

Advanced Coursework
The Bachelor of Advanced Studies advanced coursework option consists of 48 credit points, with a minimum of 24 credit points at 4000-level or above. Of these 24 credit points, you must complete a project unit of study worth at least 12 credit points.

Meritorious students may apply for admission to Honours within a subject area of the Bachelor of Advanced Studies. Admission to Honours requires the prior completion of all requirements of the Bachelor's degree, including Open Learning Environment (OLE) units. If you are considering applying for admission to Honours, ensure your degree planning takes into account the completion of a second major and all OLE requirements prior to Honours commencement.

Unit of study requirements for Honours in the area of Statistics: completion of 24 credit points of project work and 24 credit points of coursework.

Contact and further information


First year enquiries email:
Other undergraduate enquiries email:
All enquiries 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:

  1. Exhibit a broad and coherent body of knowledge in fundamental principles of probability theory and statistics, including the principles of decision-making under uncertainty and statistical hypothesis testing.
  2. Exhibit a deep and comprehensive knowledge of statistical reasoning and inference methods, the framework of statistical hypothesis testing and common statistical procedures.
  3. Formulate statistical questions in a disciplinary context and identify and apply appropriate techniques and statistical reasoning to prepare and analyse data.
  4. Analyse data in descriptive, interpretive and exploratory ways using graphical methods and visualisation tools.
  5. Identify and address gaps in their statistical knowledge and skills by independently sourcing, collating and synthesising appropriate resources that extend their understanding of statistical concepts.
  6. Communicate statistical concepts, methodology and results to diverse audiences using a variety of models including to facilitate data-driven decision-making.
  7. Use computer resources and statistical programming languages to address a broad range of statistical questions.
  8. Construct robust experimental designs using statistical principles.
  9. Address practical and abstract statistical problems using a range of concepts, techniques and technologies, working professionally, ethically and responsibly and with consideration of cross-cultural perspectives, within collaborative, interdisciplinary teams.