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.
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.
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 (160.1xx or 228.171; and one of 161.120 Introductory Statistics, 161.130 Introductory Biostatistics, 161.101 Statistics for Business, or their equivalents).
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) the compulsory courses listed in the Schedule for the Qualification;
(d) 45 credits from Schedule A courses;
(e) at least 75 credits from Schedule B and Schedule C courses;
(f) no more than 30 credits from Schedule C courses;
(g) 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.
4. The Graduate Diploma in Applied Statistics is awarded without specialisation.
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.
7. The timeframes for completion as outlined in the General Regulations for Undergraduate Degrees, Undergraduate Diplomas, Undergraduate Certificates, and 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.
9. The general Unsatisfactory Academic Progress regulations will apply.
161.200 Statistical Models 15 credits
P 160.1xx or 228.171 and one of 115.101, 161.100 - 161.130 R 161.231
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
161.220 Data Analysis 15 credits
P One of 161.101, 161.111, 161.120, 161.122 or 161.130 R 161.250
161.223 Introduction to Data Mining 15 credits
P One of 115.101, 161.100-161.130 R 161.324, 161.326, 161.777
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
161.304 Advanced Statistical Modelling 15 credits
P 161.200
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 and 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
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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Last updated on Tuesday 08 October 2019
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