Master of Applied Data Science
Data science is a new profession emerging along with the exponential growth in size and availability of 'big data'. A data scientist provides insight into future trends from looking at past and current data. Data science is an essential skillset in a world where everything from education to commerce, communication to transport, involves large-scale data collection and digitalisation.
This conversion Master's is designed to accommodate students from a range of backgrounds (not just those with Mathematics, Statistics, and Computer Science majors), who want to enhance or build their data science capabilities and combine these with the skills and knowledge they bring from their previous studies. So long as you are data-hungry and industry-aware; this degree can add to your employability and career prospects.
- One of the few such programmes in Australasia that supports the development of students from a wider undergraduate background.
- Work-integrated learning is a big component of the degree – you will work on an industry data science project.
- There is also a focus on broader skills required of data scientists such as advanced analytical capability, problem solving, critical thinking, teamwork, and communication skills.
- UC has strengths in the area of data science, including a number of relevant research centres (eg, Toi Hangarau | Geospatial Research Institute, Tangata Hangarau, Hangarau Tangata | HIT Lab NZ, Wireless Research Centre, Digital Humanities).
Potential students can come from a variety of undergraduate backgrounds. You will need a B Grade Point Average in 300-level bachelor's degree courses, or have evidence of achievement at postgraduate level.
If English is your additional language, you are also required to meet UC's English language requirements.
For the full entry requirements, see the Regulations for the Master of Applied Data Science or use the admission requirements checker.
How to apply
You can apply online at myUC. Find out more about how to apply for graduate and postgraduate qualifications.
The degree will comprise a minimum of 180 points as follows:
- up to three Foundation Courses
- four courses from advanced data science competencies
- remaining courses from relevant elective options
- DATA601 Applied Data Science Project (45 points).
You can study this qualification full-time in a minimum of 1 year, and part-time in a maximum of 3 years.
You will be required to enrol in all these foundational courses unless there is evidence of prior learning in the fundamentals of data science (exemptions must be approved by the Programme Director).
The courses are:
Advanced Data Science Competencies
All students take the following courses (unless the Programme Director approves a substitution).
You will enrol in DIGI405 and STAT462 before enrolling in DATA420 and STAT448:
Compulsory courseDATA601 Applied Data Science Project (45 points)
This compulsory project involves working in a team to solve real-world data science problems.
The remaining courses will be any relevant 400 or 600-level courses in: Biological Sciences, Computer Science, Data Science, Digital Humanities, Economics, Environmental Science, Finance, Geography, Geology, Mathematics, Physics, Psychology, Statistics. Or in any other relevant subject as approved by the Programme Director and the relevant Head of Department.
Some examples of elective courses students have enrolled in recently include:
- COSC421 Advanced Topics in Security
- COSC424 Secure Software
- COSC428 Computer Vision
- DATA415 Computational Social Choice
- DATA416 Contemporary Issues in Data Science
- DATA417 The Trustworthy Data Scientist
- DATA419 Online Communities and Social Networks
- DATA422 Data Wrangling
- DATA423 Data Science in Industry
- DATA424 Information is Beautiful
- DATA425 Foundations of Deep Learning
- DATA430 Medical Data Informatics
- BIOL459 Genomics
- GISC404 Spatial Analysis
- GISC412 Spatial Data Science
- GISC422 Foundations of Geographic Information Systems
- HLTH462 Quantitative Health Methods
- INFO620 Information Systems Management
- INFO634 Data Analytics and Business Intelligence
- POLS443 Science, Technology and Environmental Policy
- STAT446 Generalised Linear Models
- STAT447 Official Statistics
- STAT450 Advanced Statistical Modelling
- STAT455 Data Collection and Sampling Methods
- STAT456 Time Series and Stochastic Processes
- STAT463 Multivariate Statistical Methods
According to industry experts, data scientists know what the technology can offer, what analytics are possible, and can communicate on all those aspects to the wider business. This applies no matter the field or sector, as data and analytics have become so important and integral to organisational decision making. Graduates will be ready to work in a range of industries including: government, corporates, the IT sector, market research and finance, agriculture, and transport.
Aotearoa New Zealand and other countries are currently experiencing a skills shortage in this area, and the need for data-savvy professionals with applied experience is growing.
An Applied Data Science graduate's skills will include:
- advanced knowledge in data science
- the ability to use data to inform workplace solutions
- planning and implementing data-informed projects
- working in multidisciplinary teams
- have an understanding of what programming can offer.
- Read what other UC postgraduate students have gone on to achieve in their studies and careers in our student and graduate profiles.
- Te Rōpū Rapuara | UC Careers can help you to achieve the career you want, connect with employers, or find a job.
- For research into career destinations by qualification, visit Te Pōkai Tara | Universities New Zealand website.
- Find out more about what you can do with a degree from UC.
- Come along to an upcoming information event for prospective postgraduate students.
For full requirements, see the Regulations for the Master of Applied Data Science.
For study planning help, contact Te Kura Pāngarau | School of Mathematics and Statistics.
Te Kaupeka Pūtaiao | Faculty of Science
Te Whare Wānanga o Waitaha | University of Canterbury
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