Price
Domestic learners
$1,176* per 15 point course
International learners
$5,175* per 15 point course
*Fee estimates are based on the 2025 fee schedule and are subject to revision each year. Actual fee will be determined by course selection. Prices include GST where applicable. Non-tuition fees, such as the Student Services Levy (SSL), will also apply.
Qualification
MADS: 180 points
PGDipADS: 120 points
Duration
Master of Applied Data Science (MADS): 3 years part-time, 1.5 years full-time
Postgraduate Diploma in Applied Data Science (PGDipADS): 2 years part-time, 1 year full-time
Entry times
3 February 2025
14 July 2025
2026
Overview
In our rapidly changing world, data is being captured, stored and shared every day. Being able to use and understand the vast amounts of information that we access can lead to better productivity; and efficiency, and is increasingly necessary for effective leadership.
Skills in data science and data management are in hot demand around the world, and graduates will stand out from the crowd with applied skills, technical knowhow, and analytical expertise.
With flexible study options, you can choose as you learn whether you'd like to graduate with a Postgraduate Certificate in Applied Data Science, or progress through to complete a Master of Applied Data Science.
With the Master of Applied Data Science, you'll test your skills through a research project, giving you a head start into your next career move.
With the Postgraduate Diploma in Applied Data Science, you'll gain a broad understanding of the principles of data science, the software tools that can be used to track and describe data trends, as well as analytical capability in a fast-tracked timeframe.
Requirements
To ensure that our learners have the necessary background and experience to succeed, you will need to have:
- Qualified for a university degree in an area which is relevant to data science - e.g. biological sciences, computer science, digital humanities, economics, environmental science, finance, geography, geology, mathematics, physics, psychology, statistics, or any other relevant degree subject to approval of the Amo Matua, Pūtaiao | Executive Dean of Science or delegate; and
- Passed 90 points in relevant 300-level courses with at least a B Grade Point Average; and
- Been approved as a student for the degree by the Amo Matua, Pūtaiao | Executive Dean of Science or delegate; and
- Met any other prerequisites specified in the Regulations for the Postgraduate Diploma in Applied Data Science or the Regulations for the Master of Applied Data Science.
To enter the master's degree programme, you will also need to confirm placement availability, subject to approval.
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 Postgraduate Diploma in Applied Data Science, the Regulations for the Master of Applied Data Science, or use the admission requirements checker.
Unsure about your suitability?
As part of our application process, your eligibility will be assessed by our data science academic team to make sure that your academic and/or professional background meets the entry criteria. Unfortunately our Tuihono UC | UC Online team cannot confirm your eligibility before your application is submitted, beyond referring you to the requirements above. We are happy to help answer any general questions you have about the programme or online learning, however. You can get in touch with us here.
Structure
The Postgraduate Graduate Diploma in Applied Data Science can be studied part-time over 2 years, or full-time over 1 year, subject to course availability. The programme must be completed within 4 years.
The Master of Applied Data Science includes an additional 15-point paper, and a 45-point research project course. It can be studied part-time over 3 years, or full-time over 1.5 years, subject to course availability. Sign up to our mailing list to find out more.
Time commitment
Unless otherwise stated, Tuihono UC | UC Online learners study across terms, rather than semesters. We have four terms per year which consist of nine-weeks of study (including a one-week study break), followed by a two-week period of marking and feedback.
Part-time learners complete one 15-point course every term*, requiring approximately 18.5 hours of study per week. Full-time learners complete two courses every term*, requiring approximately 37.5 hours of study per week. Study time includes taking in course material, reflection time and writing assessments. Our courses are flexible, enabling you to plan your study around your other commitments.
*Note: The course load differs when studying PHIL426 - Data Ethics (10 points) and MBUS655 - Project Management Fundamentals (5 points), as these two smaller-point courses are taken concurrently.
Upcoming term dates
Our current nine-week learning dates can be found below (please note: these dates exclude our two-week period of marking and feedback).
- 3 February - 6 April 2025
- Study break: 3-9 March 2025
- 28 April - 29 June 2025
- Study break: 26 May - 1 June 2025
- 14 July - 14 September 2025
- Study break: 11-17 August 2025
- 29 September - 30 November 2025
- Study break: 27 October - 2 November 2025
Please note: these dates are provisional and may be subject to change.
Begin
Get started with 3 foundation courses to begin your data science learning journey.
Courses taken: Introduction to Data Science (DATA401), Computer Programming (COSC480), and Data Management (MBIS623).
Diploma
Having successfully completed the foundation courses, take the next step in your learning and complete the six courses you need to graduate with a Postgraduate Diploma in Applied Data Science.
Courses taken: Scalable Data Science (DATA420), Big Data (STAT448), Texts, Discourses and Data (DIGI405), Data Mining (STAT462), Data Ethics (PHIL426) and Project Management Fundamentals (MBUS655).
Master
If you would like to upgrade your PGDipADS into an 180-point Master of Applied Data Science, you need to complete all of the programme above plus two more courses.
Courses taken:
- 1 x 15-point course from Data Wrangling for Data Science (DATA422) OR Computational Social Choice (DATA415)
- plus a 45-point research project (DATA601).
Entry to the master's degree is not automatic but will be based on your grades in the previous courses and is subject to approval.
Please note: our courses may not be offered in this order, depending on availability.
What you'll study
The Postgraduate Diploma is comprised of nine NZQF level 8 postgraduate courses detailed below. The Master of Applied Data Science is comprised of those same nine courses, plus one additional 15-point course at NZQF level 8 (DATA422 or DATA 415), concluding with a 45-point research project at NZQF level 9 (DATA601).
Description
This course covers the development of statistical concepts and their application to complex systems.
Description
An introduction for graduate students to imperative computer programming using Python. Topics include: expressions, assignment, selection and iteration, structured data (lists, dictionaries, tuples, arrays), functional decomposition, file processing, using library code, and an introduction to object-oriented programming. Students must develop a significant piece of program code in a project that demonstrates mastery of programming for practical applications, typically in data science.
Description
This course introduces students to a range of topics that underpin the successful use and management of databases in contemporary organisations. The course exposes the students to associated real life issues related to data management and database management systems.
Description
This course will introduce students to new computational methods used in data science. We will look at methods for data from a range of contexts, including scalable methods used for big data and distributed computing.
Description
Suited to anyone with an interest in data, and how it can be used in decision making, this course will introduce you to big data and some of the techniques you can use to access, explore and investigate it.
Description
This course examines computer-aided methods used in digital humanities and the social sciences for analysing discourses, an object of study that draws together multiple ways that language reflects and shapes social meanings. Within this context, it introduces concepts and methods for analysing natural language data and applies these through a series of practical lab classes. The first part of the course focuses on classic discourse analysis methods drawn from corpus linguistics, as well as the essential preprocessing steps used to prepare texts for a range of analytical purposes. In the second part of the course we study topic modeling, a technique for unsupervised, exploratory data analysis that has been widely used in digital humanities, and, finally, consider supervised text classification methods to identify discursive attributes such as sentiment, genre, or style.
Description
Suited to anyone with an interest in analysing large datasets. This course will introduce a variety of statistical learning and data mining techniques for classification, regression, clustering and association purposes. Possible topics include, classification and regression trees, random forests, Apriori algorithm, FP-growth algorithm and support vector machines.
Description
This course is designed to equip you with foundational knowledge and skills needed to navigate the complex ethical considerations that arise in the rapidly evolving field of data science. You’ll learn to identify, evaluate, and mitigate ethical data issues, exploring concepts such as autonomy, wellbeing, justice, confidentiality, and informed consent. Using a case-based approach, you’ll become confident using data ethics principles at every stage of data analysis to guide your practice, from planning, processing, and sharing analyses. This course has a particular focus on data sovereignty, exploring how data ethics and Te Tiriti o Waitangi connect, looking at Maori sovereignty, partnership and justice. You’ll walk away with a framework to guide your work with data, helping ensure that your processes are appropriate, ethical and impactful.
Note that this course will be studied at the same time as MBUS655 - Project Management Fundamentals.
Description
Learn how to ensure project management success learning the skills you need to deliver projects on-time and on-budget, with positive stakeholder engagement.
Note that this course will be studied at the same time as PHIL426 - Data Ethics.
This course develops learners' skills in data cleaning and processing, data integration techniques and implementing data wrangling workflows for a real-world datasets.
This course provides a thorough introduction to both classical and computational social choice. Social choice theory is the study of mechanisms for collective decision making, such as voting rules or protocols for fair division. Computational social choice addresses problems at the interface of social choice theory with computer science, it uses concepts from social choice theory in the presence of big datasets. This course will introduce some of the fundamental concepts in social choice theory and how they are used in today's data science. The topics covered include material in voting theory, preference aggregation, judgment aggregation, and fair division.
This applied research project will give you the skills and experience to work in a team to solve real world data science problems.
How does the research project work? Some learners source their own projects through their employer (with supervision from an academic at UC), while others carry out a research project from within UC.
If you would like to study the master’s option, entry would be based on your grades in the courses required for the postgraduate diploma, and you would need to apply before graduating with the diploma. Please register your interest to find out more.
Our people
The postgraduate programmes in Applied Data Science are coordinated by Gabor Erdelyi, Clemency Montelle, Taylor Winter, and Jennifer Brown.
Gabor Erdelyi’s primary research interest is in the theoretical foundations of artificial intelligence (AI), in particular in multi-agent decision-making and algorithmic decision theory. He is also interested in the societal and regulatory aspects of AI. In addition to his role at UC, he is serving as an expert at the International Organization for Standardization’s (ISO) AI committee (ISO/IEC JTC 1/SC 42), actively shaping standardization on AI.
Gabor is also an affiliate of the International Panel on the Information Environment, providing neutral assessments on the condition of the global information environment and evaluate the best policy solutions for addressing threats to it.
Clemency Montelle's research interests include the history and philosophy of mathematics; the preparation, translation, and commentary of ancient mathematical texts in Greek, Latin, Sanskrit, Arabic and Akkadian; and ancient mathematical astronomy and modelling.
Taylor Winter is currently investigating how different societal and group factors can elicit transient changes in an individuals levels and types of authoritarian state. He uses a range of longitudinal and momentary data with a focus on Bayesian methods and structural equation modelling.
More broadly, Taylor uses a number of data sources, including surveys, administrative data, and experimental data, to understand wellbeing. This is a complex topic that can involve a broad range of variables such as an individuals environment, relationships, or even physical health.
Jennifer Brown's primary research interest is in environmental statistics and she has expertise in survey design and environmental monitoring. Research in environmental statistics is collaborative by its very nature, and Jennifer works with both statisticians and biologists. As an applied statistician, Jennifer is involved in projects in a variety of application areas and as such publishes in a wide range of journals.
Jennifer's research interests are broad and go beyond environmental statistics. She works in other application areas, most recently, human health and wellbeing. She also works in theoretical statistics, with publications in topics ranging from theoretical sampling to regression trees.
Stay updated
If you’re keen to know more, stay updated on when enrolments open or ask a question, please sign up for updates below.
Cap & minimum enrolment threshold: a minimum number of learners is needed for effective interaction and feedback, while a maximum cap of learners ensures high quality learning and support. If the minimum number of enrolments required for a course isn’t met, or the maximum cap is exceeded, learners will be given the option to defer their study or receive a refund.
FAQ
Whether you need advice finding the right course for you or support with the enrolment process, we’re here to help! Contact enrolment support for course information, technical help and enrolment support.
The Tuihono UC | UC Online Postgraduate Diploma in Applied Data Science is a fast-tracked programme designed for those with non-technical backgrounds to develop advanced knowledge, skills and competencies in data science.
The Tuihono UC | UC Online Master of Applied Data Science is a taught master's degree to enable you to move into your next career step in Data Science, and is designed for those with non-technical backgrounds.
Bringing together the latest learning applicable to your life and career, the Tuihono UC | UC Online postgraduate study in Applied Data Science gives you the same quality education as our on-campus programmes, with the flexibility of online learning.
This includes 24/7 learning, academic advice and technical support, giving you the support to study anywhere, anytime, at your pace.
Aotearoa New Zealand and many other countries are currently experiencing a skills shortage in this area, and the need for data savvy professionals with applied experience is growing.
Graduates will be ready to work in a range of industries including: government, corporates, the IT sector, market research and finance, agriculture, and transport.
Learn more about Postgraduate Diploma in Applied Data Science career opportunities here.
Learn more about Master in Applied Data Science career opportunities here.
The estimated tuition fees per course based on the 2025 fee schedule:
- for domestic learners is $1,176* per 15 point course
- for international learners is $5,175* per 15 point course
Total programme investment for the 120-point PGDipADS based on the 2025 fee schedule:
- for domestic learners is $9,410*
- for international learners is $41,400*
Total programme investment per year for the 180-point MADS based on the 2025 fee schedule:
- for domestic learners is $14,115*
- for international learners is $62,100*
*Fee estimates are based on the 2025 fee schedule and are subject to revision each year. Actual fee will be determined by course selection. Prices include GST where applicable. Non-tuition fees, such as the Student Services Levy (SSL), will also apply.
Student Services Levy costs
Each year university students around Aotearoa New Zealand are charged a Student Services Levy (SSL) in addition to their tuition fees. All the SSL money collected can only be used for the benefit of students - never for academic or administrative costs.
The SSL is automatically calculated on how many points you enrol in per academic year, capped at a maximum of 150 points. Tuihono UC | UC Online learners are charged a reduced SSL rate, which is 20% of the usual on-campus student levy. This is calculated as $1.94 per academic point in 2025. You can learn more about the Student Services Levy here, and more about UC Support Services here.
The PGDipADS can be completed full-time in one year, or part-time over two years, subject to course availability. It must be completed within four years.
The MADS can be completed full-time in one and a half years, or part-time over two and a half years, subject to course availability. It must be completed within three years.
Studying online allows for flexibility in completing coursework, and Tuihono UC’s programme provides learners with the same quality education and resources as our on-campus programmes.
A taught master's degree can give you the next step in your career, or support you to shift into a new career in data science.
The robustness of the academic programmes are the same, but the online programmes offer greater flexibility in terms of scheduling and location.
Each programme is delivered with a majority of coursework online, using a range of technologies including video conferencing, online discussion forums, and interactive learning experiences.
Learners have access to the same support services as on-campus students, including academic advising, technical support, and library resources. There are also great resources for both Canterbury based and remote learners at the UC RecCentre. Learn more here.
Learners also have the support of our Tuihono UC Learner Experience team.
There are a range of options you can use to finance your study, which you can learn more about here.
Applications are made online through our UC Online website – view open enrolments and/or expressions of interest for when enrolments open.
If you are a domestic learner, the below process will apply. If you are an international learner, the application process is slightly different, and we recommend getting in touch with our enrolment team at info@uconline.ac.nz to answer any questions.
For domestic learners, your application will be assessed by our academic team to make sure you meet all entrance criteria (including academic and English language requirements). We may be in touch to ask you further questions about your experience, or to request additional supporting documentation. If your application is accepted, you will be sent an 'Offer of Place' to let you know your enrolment has been conditionally approved.
Following this, we will generate an ‘Enrolment Agreement’ outlining your courses, fees and student agreement, which you need to sign and accept. Your enrolment is only complete when the fees outlined in this agreement are paid in full (view payment options), at which point you’ll become ‘Fully Enrolled’ and receive a ‘Welcome to Tuihono UC | UC Online’ email with details of your next steps to start learning. If you have any questions during the enrolment process, please get in touch with our team via info@uconline.ac.nz.
Learners are expected to participate actively in forum discussions, and complete assessments on time.
Each programme is a standalone professionally relevant qualification.
Any learner interested in pursuing a PhD or further research would be encouraged to contact the Faculty of Science advisors.
An on-campus version of the PGDipADS as well as an on-campus version of the MADS are both available through the University of Canterbury.
We are also developing our online offerings - if you'd like to hear about our latest news and offers, join our mailing list.
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