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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. 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.
Unsatisfactory academic progress
9. The general Unsatisfactory Academic Progress regulations will apply.
Schedule for the Graduate Diploma in Applied Statistics
Course planning key
- Prerequisites
- Courses that need to be completed before moving onto a course at the next level. For example, a lot of 200-level courses have 100-level prerequisite courses.
- Corequisites
- Courses that must be completed at the same time as another course are known as corequisite courses.
- Restrictions
- Some courses are restricted against each other because their content is similar. This means you can only choose one of the offered courses to study and credit to your qualification.
Key terms for course planning
- Courses
- Each qualification has its own specific set of courses. Some universities call these papers. You enrol in courses after you get accepted into Massey.
- Course code
- Each course is numbered using 6 digits. The fourth number shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).
- Credits
- Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.
- Specialisations
- Some qualifications let you choose what subject you'd like to specialise in. Your major or endorsement is what you will take the majority of your courses in.
Schedule A
Course selection (Choose 15 credits from)
Course code: 161250 Data Analysis 15 credits
Biology, psychology, and other sciences require statistical methods for analysing and visualising data. This course is designed to be accessible to students from any discipline, first building a deeper understanding of fundamental statistical concepts, then teaching a range of practical approaches for exploring statistical relationships, testing hypotheses, evaluating models, and presenting conclusions.
View full course detailsCourse selection (Choose 15 credits from)
Course code: 161251 Regression Modelling 15 credits
Common data analysis and regression techniques for application in science, business and social science. Topics include simple and multiple regression; linear models with categorical explanatory variables; model diagnostics; inference for linear models; polynomial regression; models for time dependence; methods for variable selection; non-linear and weighted regression.
View full course detailsCourse selection (Choose 15 credits from)
Course code: 161222 Design and Analysis of Experiments 15 credits
The planning, conduct and analysis of scientific experiments, using examples from chemical, biological, genomic, and engineering sciences. Manipulation and visualisation of experimental data; advantages and disadvantages of various designs; coping with missing data and practical constraints. Introduction to design techniques and concepts including randomisation, blocking, structured treatments, balance and orthogonality, crossed and nested effects, pseudo-replication.
View full course detailsCourse code: 233214 GIS and Spatial Statistics 15 credits
Introduction to handling and analysis of digital geospatial data. Operation of GIS software, including collection, processing and understanding of data, production of maps and geospatial projection systems. Integration of spatial statistical software with GIS. Introduction to appropriate spatial statistics techniques including kernel smoothing, kriging, point processes and spatially correlated areal data.
View full course detailsSchedule B
Course code: 161304 Statistical Modelling 15 credits
This course covers the ideas underlying statistical modelling, its implementation through computational methods, and links to practical applications. Topics include probability and random variables, models for discrete and continuous data, model selection, model fitting and goodness of fit, model inference, and introduction to stochastic processes.
View full course detailsCourse code: 161323 Multivariate Analysis 15 credits
This course teaches methods to understand patterns and structures inherent in data sets containing many variables. The fundamentals of data visualisation, clustering, and dimension reduction with examples taken from a range of applications.
View full course detailsCourse code: 161324 Data Mining 15 credits
A practical approach to data mining with large volumes of complex data; prepare, cleanse and visualise data; supervised and unsupervised modelling; ensemble and bundling techniques; use of leading software tools.
View full course detailsCourse code: 161331 Biostatistics 15 credits
Sciences such as biology and medicine yield data that require a wide range of statistical techniques, including standard linear models and their extensions. Case studies are used to demonstrate topics such as nonlinear regression, linear models for binary and count data, and mixed effects models. Emphasis is placed on application of appropriate statistical techniques through extensive use of statistical software.
View full course detailsCourse code: 161390 Special Topic 15 credits
Schedule C
Course code: 161380 Statistical Analysis Project 15 credits
The course provides an opportunity for students to gain statistical research experience. Working with an academic staff member, students will develop a short research proposal, carry out the proposed research, and present their findings in an agreed manner.
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