Regulations for The Master of Analytics - MAnalyt

Official rules and regulations for the Master of Analytics. These regulations are for the 2025 intake to this qualification.

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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 Postgraduate Degrees, Postgraduate Diplomas, and Postgraduate Certificates.

Part II

Admission

1. Admission to the Degree of Master of Analytics requires that the candidate will:

(a) meet the University admission requirements as specified; and

(b) have sufficient background in statistical analysis tools to satisfy the Academic Board that they have the capacity to undertake the programme; and

(c) have been awarded or qualified for a Bachelor’s degree in a relevant subject, with a B- grade average across the higher level courses, or equivalent; or

(d) have been awarded or qualified for a Bachelor’s degree, and have completed at least two years’ experience in a relevant field of work or professional           activity, or equivalent.

Qualification requirements

2. Candidates for the Degree of Master of Analytics shall follow a parts-based programme of study, which shall consist of courses totalling at least 180 credits, comprising:

(a) Part One and Part Two as defined by the Schedule to the Qualification;

and including:

(b) any compulsory courses listed in the Schedule for the Qualification;

(c) at least one subject;

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

Specialisations

3. Candidates may complete a subject by passing at least 120 credits in a specialisation including the Applied Analytics Project from Part Two. The requirements for each subject are set out in the Schedule for the Qualification.

Approved subjects are: Business, Health, Public Policy

Academic requirements

4. Every candidate for the Degree of Master of Analytics shall complete to the satisfaction of the Academic Board, a minimum of 600 hours in approved practica and associated reports in accordance with the following courses:

115.801 Applied Analytics Project

115.802 Applied Analytics Project Part 1

115.803 Applied Analytics Project Part 2

Student progression

5. In cases of sufficient merit, the Degree of Master of Analytics may be awarded with Distinction or Merit.

6. For progression from Part One to Part Two, candidates must have maintained a B- grade average over the Core compulsory courses undertaken in Part One.

Completion requirements

7. The timeframes for completion as outlined in the General Regulations for Postgraduate Degrees, Postgraduate Diplomas and Postgraduate 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 a relevant postgraduate diploma should they meet the relevant Qualification requirements.

Unsatisfactory academic progress

9. The general Unsatisfactory Academic Progress regulations will apply.

Transitional provisions

10. Subject to any Maximum Time to Completion and the Abandonment of Studies provisions specified in the Part I regulations for the degree, candidates enrolled in the Health subject within the Master of Analytics prior to 1 January 2022 who have successfully completed 250.702 or 250.704 may substitute either of these for 250.706 in order to fulfil the subject requirements until 31 December 2025.

11. Subject to Maximum Time to Completion and the Abandonment of Studies provisions specified in the Part I regulations for the degree, candidates enrolled in the Health subject within the Master of Analytics prior to 1 January 2025 who have successfully completed at least 30 credits towards the subject requirements, will be able to complete the qualification under the 2024 Calendar regulations. In addition, candidates may substitute course(s) 250701, 250703 and 250706 with 250705 and 250711. These transitional arrangements expire 31 December 2026.

Schedule for the Master of Analytics

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.

Part One

Core compulsory courses

Course code: 158739 Data Mastery: Scripting, Databases and Data Privacy 15 credits

An introduction to the field of analytics, including the process of identifying an analytics problem in context, identifying sources and acquiring data, preparing data for analysis to address the problem. Emphasis is placed on developing programming skills relevant for data processing and data retrieval from databases. Special attention is given to privacy, security and ethical considerations surrounding data, and to communication of results.

Restrictions: 161750

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Course code: 161762 Multivariate Analysis for Big Data 15 credits

Research methods suitable for the analysis of big datasets containing many variables. The fundamentals of data visualisation, customer segmentation, factor analysis and latent class analysis with examples taken from business and health fields. Emphasis will be placed on achieving a conceptual understanding of the methods in order to implement and interpret the outcomes of multivariate analyses.

Restrictions: 161323, 161772

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Course code: 161777 Practical Data Mining 15 credits

A practical approach to data mining with large volumes of complex data; prepare, cleanse and explore data; supervised and unsupervised modelling with association rules and market basket analysis, decision trees, multi-layer neural networks, k-nearest neighbours, k-means clustering and self-organising maps, ensemble and bundling techniques, text mining; use of leading software tools; business examples and research literature.

Restrictions: 161223 and 161324

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Course code: 178724 Applied Econometric Methods 15 credits

This course covers the specification, estimation and validation of econometric models for analysis and forecasting, incorporating in-depth discussions regarding the treatment of common problems encountered in data analysis.

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Subject courses

Part Two (Choose 60 credits from)

Choose 60 credits from
Course code: 115801 Applied Analytics Project 60 credits

Under the supervision of academic staff, students work with an external organisation on the application of computer-based analytics tools to a project in the domain of business analytics, healthcare system analytics, or public policy analytics. Special attention is given to privacy and ethical considerations, and to the (visual) communication of results.

Restrictions: 115802, 115803

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Course code: 115802 Applied Analytics Project Part 1 30 credits

Under the supervision of academic staff, students work with an external organisation on the application of computer-based analytics tools to a project in the domain of business analytics, healthcare system analytics, or public policy analytics. Special attention is given to privacy and ethical considerations, and to the (visual) communication of results.

Restrictions: 115801

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Course code: 115803 Applied Analytics Project Part 2 30 credits

Under the supervision of academic staff, students work with an external organisation on the application of computer-based analytics tools to a project in the domain of business analytics, healthcare system analytics, or public policy analytics. Special attention is given to privacy and ethical considerations, and to the (visual) communication of results.

Corequisites: 115802 Restrictions: 115801

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