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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

No new enrolments

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 160101, 160102, 160103, 160105, 160111, 160112, 160131, 160132, 160133, 228171; and one of 161111, 161122, 161101, 161140, 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. Any 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.

Academic Progress

9. The general Unsatisfactory Academic Progress regulations will apply.

Transitional Provisions

10. Subject to any Maximum Time to Completion regulations and the 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 161200 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)

161220 Data Analysis 15 credits
P 161101, 161111, 161120, 161122 or 161130 R 161250

161250 Data Analysis 15 credits
P 1611xx R 161220

Course selection (15 credits from)

161221 Applied Linear Models 15 credits
P (One of ( 161122 or 161220 or 233214) and one of ( 160101 or 160102 or 160105)) or one of 161101, 161120 or 161130 R 161251

161251 Regression Modelling 15 credits
P 1611xx R 161221

Course selection (15 credits from)

161222 Design and Analysis of Experiments 15 credits
P 1611xx R 161322

161223 Introduction to Data Mining 15 credits
P One of 115101, 161100-161130 R 161324, 161326, 161777

233214 GIS and Spatial Statistics 15 credits
P 161111 or 161122 R 233251, 233301

Schedule B

161303 Probability and Random Processes 15 credits
P ( 160101 or 160102 or 160105) and ( 161122 or 161220)

161304 Statistical Modelling 15 credits
P ( 160101 or 160102 or 160105) and ( 161122 or 161250 or 161251 or 161220 or 161221)

161305 Statistical Inference 15 credits
P 161303

161306 Advanced Data Analysis 15 credits
P 161221 R 161331

161312 Statistical Machine Learning 15 credits
P ( 161111 or 161122) and ( 159101 or 159171) R 161326, 161324

161321 Sampling and Experimental Design 15 credits
P One of 1612xx R 161322

161322 Design and Analysis of Surveys and Experiments 15 credits
P One of 1612xx R 161775, 161321 and 161331

161323 Multivariate Analysis 15 credits
P One of 161222, 161220, 161221, 161250, 161251, 233214 R 161762

161324 Data Mining 15 credits
P One of 161122, 161220, 161221, 161250 or 161251 R 161223 and 161777

161325 Statistical Methods for Quality Improvement 15 credits
P One of 161200, 161220, 161230, 161240

161327 Generalised Linear Models 15 credits
P 161221 and one of 1601xx R 161726

161331 Biostatistics 15 credits
P 161250 or 161251 or 161220 or 161221 R 161327, 161778

161342 Forecasting and Time Series 15 credits
P 161220 or 161221 or 161250

161390 Special Topic 15 credits

Schedule C

161380 Statistical Analysis Project 15 credits
P Two 1613xx courses

161382 Statistical Analysis Project 30 credits
P Two 1613xx courses