Master of Professional Studies specialisation in Data Science - Taught 120

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The last decade has seen an explosion in the amount of data available. It has evolved into the most important asset for many companies and people. Big data is ubiquitous, but to extract information, individuals require the ability to both manage and analyse the data. This ability to turn data into information, knowledge and innovative products often separates success from failure.

Data scientists have the skill set to drive innovation and affect the success of start-ups, established businesses and organisations, governments and science projects, as well as media, broadcasting and cultural events.

The Data Science major will provide initial preparation for students wishing to pursue a career in this area. It ties together courses from Computer Science, Statistics and Mathematics to provide the necessary background and training.

The Department of Statistics is the birthplace of the R Project. Founded in 1996 by Associate Professors Robert Gentleman and Ross Ihaka. R is a programming language and environment for statistical computing and graphics. It is taught around the world and is used by Ivy League universities, Google, Uber, and many more organisations.

The Master of Professional Studies specialising in Data Science concentrates on the application of data management and data analysis skills to a professional domain, such as business.

On the world stage, Data Science is a rapidly growing field with an unmet demand for suitably qualified graduates.
- Data analyst
- Data scientist
- Database administrator
- Developer
- Information officer
- Insight manager
- Statistician

The programme for students enrolling in the Data Science specialisation requires the approval of the Head of Department of Statistics or the Head of Department of Computer Science and the Dean of Faculty of Science.

Complete 120 points comprising:

At least 30 points from Group 1 Elective Courses, and
At least 30 points from Group 2 Elective Courses, and
0 to 15 points from Group 3 Elective Courses, and
15 points for all Compulsory Course, and
Either:
30 points for all Research Project - 1 Semester, or
30 points for all Research Project - 2 Semesters

Group 1 Elective Courses

Complete at least 30 points from the following:

COMPSCI 751 - Advanced Topics in Database Systems (15 points)

COMPSCI 752 - Big Data Management (15 points)

COMPSCI 753 - Algorithms for Massive Data (15 points)

COMPSCI 762 - Foundations of Machine Learning (15 points)

Group 2 Elective Courses

Complete at least 30 points from the following:

STATS 762 - Regression for Data Science (15 points)

STATS 769 - Advanced Data Science Practice (15 points)

STATS 782 - Statistical Computing (15 points)

STATS 784 - Statistical Data Mining (15 points)

Group 3 Elective Courses

Complete 0 to 15 points from the following:

COMPSCI 705 - Advanced Topics in Human Computer Interaction (15 points)

COMPSCI 711 - Parallel and Distributed Computing (15 points)

COMPSCI 720 - Advanced Design and Analysis of Algorithms (15 points)

COMPSCI 732 - Software Tools and Techniques (15 points)

COMPSCI 734 - Web, Mobile and Enterprise Computing (15 points)

COMPSCI 760 - Advanced Topics in Machine Learning (15 points)

DIGIHLTH 701 - Principles of Digital Health (15 points)

DIGIHLTH 702 - Health Knowledge Management (15 points)

DIGIHLTH 703 - New Zealand Health Data Landscape (15 points)

DIGIHLTH 704 - Artificial Intelligence in Healthcare (15 points)

DIGIHLTH 705 - Digital Health Design and Evaluation (15 points)

DIGIHLTH 706 - Health Data Analytics (15 points)

INFOSYS 720 - Information Systems Research - Level 9 (15 points)

INFOSYS 722 - Data Mining and Big Data (15 points)

INFOSYS 727 - Advanced Information Security (15 points)

OPSMGT 741 - System Dynamics and Complex Modelling (15 points)

OPSMGT 752 - Modelling Methods in Operations Management (15 points)

SCIENT 701 - Accounting and Finance for Scientists (15 points)

STATS 707 - Computational Introduction to Statistics (15 points)

STATS 763 - Advanced Regression Methodology (15 points)

STATS 779 - Professional Skills for Statisticians (15 points)

STATS 783 - Simulation and Monte Carlo Methods (15 points)

STATS 786 - Time Series Forecasting for Data Science (15 points)

STATS 787 - Data Visualisation (15 points)

Up to 15 points may be substituted from 700 level courses relevant to the area of study with approval of the Programme Director.

Compulsory Course

Complete 15 points for ALL of the following:

DATASCI 779 - Statistical Computing Skills for Professional Data Scientists - Level 9 (15 points)

Research Project - 1 Semester

Complete 30 points for ALL of the following:

DATASCI 791 - Research Project - Level 9 (30 points)

Research Project - 2 Semesters

Complete 30 points for ALL of the following:

DATASCI 791A - Research Project - Level 9 (15 points)

DATASCI 791B - Research Project - Level 9 (15 points)