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For help with understanding these important regulations, please watch our video.

Key to the tables

P Prerequisite: Course(s) you must complete to a defined standard (or have waived) before your enrolment in another course is confirmed.

C Corequisite: Course(s) that must be completed in the same semester as another course, unless already passed or waived.

R Restriction: Similar courses, that cannot both be credited to the same qualification.

The Graduate Diploma in Applied Statistics
GradDipApplStat

Qualification Regulations

Part I

These regulations are to be read in conjunction with all other Statutes and Regulations of the University including General Regulations for Undergraduate Degrees, Undergraduate Diplomas, Undergraduate Certificates, Graduate Diplomas, and Graduate Certificates.

Part II

Admission

1. Admission to the Graduate Diploma in Applied Statistics requires that the candidate will meet the University admission requirements as specified, and shall have:

(a) been awarded or qualified for the award of a university degree; and

(b) passed approved 100 level courses in Mathematics and Statistics (one of 160.101, 160.102, 160.103, 160.105, 160.111, 160.112, 160.131, 160.132, 160.133, 228.171; and one of 161.111, 161.122, 161.101, 161.140, or their equivalents).

Qualification Requirements

2. Candidates for the Graduate Diploma in Applied Statistics shall follow a flexible programme of study, which shall consist of courses totalling at least 120 credits, comprising:

(a) courses selected from the Schedule to the Qualification;

(b) at least 120 credits at 200 level or higher, of which at least 75 credits must be at 300 level or higher;

and including:

(c) 45 credits from Schedule A courses;

(d) at least 75 credits from Schedule B and Schedule C courses;

(e) no more than 30 credits from Schedule C courses;

(f) attending field trips, studios, workshops, tutorials, and laboratories as required.

3. Notwithstanding Regulation 2, and with the permission of the Programme Director, up to 30 credits from Schedules A or B may be substituted with appropriate alternative courses, including 700 level courses.

Specialisations

4. The Graduate Diploma in Applied Statistics is awarded without specialisation.

Student Progression

5. In order to progress to courses in Schedule C candidates must have successfully completed at least 30 credits from Schedule B courses, and have achieved at least a B+ grade average over all courses previously completed towards the Graduate Diploma in Applied Statistics, in addition to meeting the pre-requisites for the selected course.

6. In cases of sufficient merit, the Graduate Diploma in Applied Statistics may be awarded with distinction.

Completion Requirements

7. The timeframes for completion as outlined in the General Regulations for Undergraduate Degrees, Undergraduate Diplomas, Undergraduate Certificates, Graduate Diplomas, and Graduate Certificates will apply.

8. Candidates may be graduated when they meet the Admission, Qualification and Academic requirements within the prescribed timeframes; candidates who do not meet the requirements for graduation may, subject to the approval of Academic Board, be awarded the Graduate Certificate in Science and Technology should they meet the relevant Qualification requirements.

Unsatisfactory Academic Progress

9. The general Unsatisfactory Academic Progress regulations will apply.

Transitional Provisions

10. Subject to the Maximum Time to Completion and Abandonment of Studies provisions specified in the Part I regulations for the degree, candidates who commenced study towards the Graduate Diploma in Applied Statistics prior to 1 January 2021 who have passed 161.200 may substitute this for a course within Schedule A. These transition arrangements expire 31 December 2023.

Schedule for the Graduate Diploma in Applied Statistics

Schedule A

Course selection (15 credits from)

161.220 Data Analysis 15 credits
P 161.101, 161.111, 161.120, 161.122 or 161.130 R 161.250

161.250 Data Analysis for Biologists 15 credits
P One of 115.101, 161.101, 161.111, 161.120 or 161.122 R 161.220

Course selection (15 credits from)

161.221 Applied Linear Models 15 credits
P (One of (161.122 or 161.220 or 233.214) and one of (160.101 or 160.102 or 160.105)) or one of 161.101, 161.120 or 161.130 R 161.251

161.251 Regression Modelling 15 credits
P One of (161.101, 161.120, 161.130, 161.140, 161.111 or 161.122) R 161.221

Course selection (15 credits from)

161.222 Design and Analysis of Experiments 15 credits
P 161.1xx R 161.322

161.223 Introduction to Data Mining 15 credits
P One of 115.101, 161.100-161.130 R 161.324, 161.326, 161.777

233.214 GIS and Spatial Statistics 15 credits
P 161.111 or 161.122 R 233.251, 233.301

Schedule B

161.303 Probability and Random Processes 15 credits
P (160.101 or 160.102 or 160.105) and (161.122 or 161.220)

161.304 Advanced Statistical Modelling 15 credits
P 161.200

161.305 Statistical Inference 15 credits
P 161.303

161.306 Advanced Data Analysis 15 credits
P 161.221 R 161.331

161.312 Statistical Machine Learning 15 credits
P (161.111 or 161.122) and (159.101 or 159.171) R 161.326, 161.324

161.321 Sampling and Experimental Design 15 credits
P One of 161.2xx R 161.322

161.322 Design and Analysis of Surveys and Experiments 15 credits
P One of 161.2xx R 161.775, 161.321 and 161.331

161.323 Multivariate Analysis 15 credits
P One of 161.220, 161.221, 161.250 or 161.251 R 161.762

161.324 Data Mining 15 credits
P One of 161.220, 161.221, 161.250 or 161.251 R 161.223, 161.312 and 161.777

161.325 Statistical Methods for Quality Improvement 15 credits
P One of 161.200, 161.220, 161.230, 161.240

161.327 Generalised Linear Models 15 credits
P 161.221 and one of 160.1xx R 161.726

161.331 Biostatistics 15 credits
P One of 161.220 or 161.221, 161.250 or 161.251 R 161.306, 161.778

161.342 Forecasting and Time Series 15 credits
P 161.220 or 161.221 or 161.250

161.390 Special Topic 15 credits

Schedule C

161.380 Statistical Analysis Project 15 credits
P Two 161.3xx courses

161.382 Statistical Analysis Project 30 credits
P Two 161.3xx courses

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