Career opportunities
Get ready to launch into a world of possibilities. With your Data Science background, you’re equipped for roles that demand analytical thinking, digital fluency and the ability to drive insight from data.
Career pathways include:
- Data analyst or data scientist roles in business, government or research
- Data visualisation and insight specialist
- Machine learning or statistical modelling developer
- Business intelligence or data-driven decision-making consultant
- Developer of data tools and infrastructure in tech, health, education or industry
How Data Science shapes the world
Discover how data science powers everything from Netflix recommendations to city planning and sports strategy. Learn what makes Otago’s programme unique and how you can get started.
Read transcript
Intro – Dr Matthew Parry:
G'day, I'm Matt from the Data Science team at Otago. So maybe you’ve heard of a Netflix problem.
Netflix have more movies and shows than you can possibly have to watch in your lifetime and even hope to search through in your lifetime. So what they do, they look through all the information they have on all the hundreds and millions of years and all the hundreds of thousands of shows they have to come up with shows they think you’ll like to watch.
And it’s not just Netflix solving problems like this using data. Second, we know communities and towns and cities collect data on air quality and water pollution, and they want to know things like this—public health going to be affected. Businesses also want to plan for the future, so they use data to track customer demand and satisfaction, what’s selling, what’s not selling.
And professional sports teams, another example, use data on player performance to figure out what would be the best team selection and also what would be the best strategy for the game.
What is Data Science
So data science is at the heart of an organization’s decision-making process. In fact, anywhere you find out are, you’ll find data science. So data science for me is that is the science of learning from data.
And data science is something for everyone. So if you like communication, graphics, visualization, then data science is for you. If you like programming and getting a computer to do all the hard work for you, then you’re like data science.
If you’re passionate about any area which involves data, then you want to do data science. And if you like number crunching like I do, then data science is for you too.
Data Science at Otago
Otago is unique. It brings together the best we’ve got—information science, computer science, and statistics. And then you get to combine it with another area of study, something you care about, and that’d be a minor or a second major in the area.
For example, you can almost do anything. So if you like commerce, you can do a commerce, any commerce option. You can do computer science, you can do marine science, you can do public health. The list just goes on and on.
In first year, there’ll be five data science papers to take. You get an introduction to information technology and information systems. You’ll get your hands dirty during practical data science. You’ll learn how to program and also get an introduction into statistics. And all this will give you plenty of time to start working towards your minor or second major.
Prerequisites
So what do you have to do to get into data science? Well, in fact, there’s no official prerequisites for data science, but we strongly recommend NCEA Level 3 digital technologies and in mathematics as being helpful.
For me, what more, it’s more important is the unofficial prerequisite—just being passionate about data. If you want to go out and get it, if you want to plot it and play with it, if you want to do something with it, then data science is for you.
And if that sounds like you, we would love to have you do Data Science at Otago.
How you will learn
Studying Data Science at Otago is dynamic and hands-on. You’ll attend lectures and tutorials, but you’ll also spend plenty of time in practical labs where you’ll code, visualise and build models.
You’ll collaborate with your peers on projects that mirror real-life scenarios: cleaning messy data, building predictive models, working in teams, and presenting insights so others can act on them. You’ll feel a part of the Otago community and your tutors aren’t just teachers, they’re active researchers and practitioners. The emphasis on combining disciplines means you’ll always see how Data Science connects to other areas you are interested in.
Recommended high school subjects for undergraduate study
There are no formal prerequisites. However, NCEA subjects like digital technology, Level 3 mathematics, and statistics are useful for building the skills and confidence needed to succeed in the programme.
Choose a study option
Whether you're embarking on your academic journey with our comprehensive undergraduate programmes or aiming to reach new heights through our advanced postgraduate offerings, Otago is here to support your aspirations.
Undergraduate qualifications
For new and current students studying towards a Bachelor's or other first degree. Explore undergraduate qualifications at Otago, designed to build a strong foundation in your chosen field, preparing you for a successful career or further study.
Bachelor of Arts (BA)
A three-year degree offering flexibility to explore a major in Arts along with other subjects
Bachelor of Science (BSc)
A three-year degree offering flexibility to explore a major in Science along with other subjects
Bachelor of Arts and Science (BASc)
Combine two majors, one in Arts and one in Science, into a four-year degree and expand your future career prospects
Bachelor of Commerce and Science (BComSc)
Combine two majors, one in Commerce and one in Science, into a four-year degree and expand your future career prospects
Bachelor of Entrepreneurship (BEntr)
A three-year degree that equips you to change the world as you create new products, services and ideas
Ready to apply?
Take the first step towards your future in this subject.
Further study opportunities
Whether you are looking to bridge your undergraduate studies to advanced knowledge or aiming to specialise in a specific field, Otago offers a range of graduate and postgraduate options to suit your aspirations.
Diploma for Graduates (DipGrad)
The Diploma for Graduates (DipGrad), requiring study of at least seven papers (at least four of which are at 300-level o...
Postgraduate study in Data Science
Explore postgraduate study in Data Science at Otago. Gain advanced knowledge, skills, and research opportunities in a su...
Related subject areas
Explore all subject areasBusiness, accounting and finance
Study business and prepare yourself for a career in the fast-paced corporate world – or even launch your own start-up
Environment, climate change and sustainability
Find creative solutions to the challenges posed by climate change and other environmental issues
Health and biomedical sciences
Transform the world by learning from top researchers and shaping the future of healthcare, medicine, and the environment
Technology, maths and computing
Study technology, mathematics, or computing to solve real-life problems – harnessing the power of information and data
Decode the future with Data Science
Step into a world where you don’t just use data, you shape it. At University of Otago, we bring together computer science and statistics in a Data Science programme designed for impact. You’ll work with real data, tackle real problems and connect your learning to the things you care about.
With expert staff who are active researchers, modern facilities and a campus vibe that supports bold ideas and collaboration, you’ll join a community that’s curious, ambitious and ready to dig in. Whether you see yourself influencing business, health, environment or technology, Otago gives you the toolkit, the support and the setting to decode the future and contribute meaningfully to today’s data-driven world.
Programme details
Compare programmes for this subject.
2025
| Year | Papers | Points |
|---|---|---|
| 100-level | COMP 101 Foundations of Information Systems | 18 |
| COMP 120 Practical Data Science | 18 | |
| COMP 161 Computer Programming | 18 | |
| COMP 162 Foundations of Computer Science | 18 | |
| STAT 110 Statistical Methods or STAT 115 Introduction to Biostatistics Note: Students are exempt from COMP 161 if they have gained entry to COMP 162 by passing COMP 151 with a grade of at least B or via an Advanced Placement Test. | 18 | |
| 200-level | COSC 201 Algorithms and Data Structures | 18 |
| INFO 204 Introduction to Data Science | 18 | |
| STAT 210 Applied Statistics | 18 | |
| STAT 260 Visualisation and Modelling in R | 18 | |
| 300-level | COSC 343 Artificial Intelligence | 18 |
| INFO 304 Advanced Data Science | 18 | |
| STAT 312 Modelling High Dimensional Data | 18 | |
| One of COSC 344 Database Theory and Applications, STAT 310 Statistical Modelling | 18 | |
| Plus | 126 further points. Up to 90 points may be taken from outside Arts. | 126 |
| Total | 360 |
2026 onwards
| Year | Papers | Points |
|---|---|---|
| 100-level | MATH 130 Fundamentals of Modern Mathematics | 18 |
| COMP 121 Computer Programming | 18 | |
| STAT 110 Statistical Methods or STAT 115 Introduction to Biostatistics | 18 | |
| DHUM 101 or one of PHIL 103, SOCI 102; MAOR 102, ANTH 105, POPH 192, BSNS 111 | 18 | |
| 200-level | DATA 201 R for Statistics and Data Science | 18 |
| DATA 202 Database Systems | 18 | |
| STAT 210 Applied Statistics | 18 | |
| COMP 220 Applied Programming Techniques | 18 | |
| 300-level | DATA 301 Applied Data Science | 18 |
| DATA 302 Statistical Learning | 18 | |
| DHUM 303 When Machines Decide | 18 | |
| STAT 310 Statistical Modelling | 18 | |
| Plus | 144 further points. Up to 90 points may be taken from outside Arts. | 144 |
| Total | 360 |
| Year | Papers | Points |
|---|---|---|
| 100-level | MATH 130 Fundamentals of Modern Mathematics | 18 |
| COMP 121 Computer Programming | 18 | |
| STAT 110 Statistical Methods or STAT 115 Introduction to Biostatistics | 18 | |
|
One of: DHUM 101(Highly recommended), PHIL 103, SOCI 102, MAOR 102, ANTH 105, POPH 192 , BSNS 111 | 18 | |
| 200-level | DATA 201 R for Statistics and Data Science | 18 |
| DATA 202 Database Systems | 18 | |
| STAT 210 Applied Statistics | 18 | |
| COMP 220 Applied Programming Techniques | 18 | |
| 300-level | DATA 301 Applied Data Science | 18 |
| DATA 302 Statistical Learning | 18 | |
| DHUM 303 When Machines Decide | 18 | |
| STAT 310 Statistical Modelling | 18 | |
| Plus | 144 further points. Up to 90 points may be taken from outside Science. | 144 |
| Total | 360 |
A minor subject can be included in many of our undergraduate degrees. To earn a minor, you typically must complete a minimum of 90 points in that subject, with at least 18 points at the 300-level.
Your minor can be a subject more commonly taken for a different degree. For example, a BCom majoring in Marketing can include Japanese as a minor subject. To include this subject as a minor in your application, first find a major subject through our Subject Search or Study Match.
You can check what’s required to receive the minor accreditation in the programme details below.
| Level | Papers | Points |
|---|---|---|
| 100-level | COMP 121 Computer Programming | 18 |
|
One of STAT 110 Statistical Methods or STAT 115 Introduction to Biostatistics | 18 | |
| 200-level | STAT 210 Applied Statistics | 18 |
|
One of COMP 220 Applied Programming Techniques or DATA 201 R for Statistics and Data Science | 18 | |
| 300-level |
One of DATA 301 Applied Data Science DATA 302 Statistical Learning | 18 |
| Total | 90 |
Papers
View a list of all related papers below.
STAT papers
| Paper Code | Year | Title | Points | Teaching period |
|---|---|---|---|---|
| STAT110 | 2026 | Statistical Methods | 18 points | Summer School, Semester 1 |
| STAT115 | 2026 | Introduction to Biostatistics | 18 points | Semester 2 |
| STAT210 | 2026 | Applied Statistics | 18 points | Semester 1 |
| STAT260 | 2026 | Visualisation and Modelling in R | 18 points | Semester 2 |
| STAT270 | 2026 | Probability and Inference | 18 points | Semester 1 |
| STAT310 | 2026 | Statistical Modelling | 18 points | Semester 1 |
| STAT311 | 2026 | Design of Research Studies | 18 points | Not offered in 2026 |
| STAT312 | 2026 | Modelling High Dimensional Data | 18 points | Semester 2 |
| STAT370 | 2026 | Statistical Inference | 18 points | Semester 2 |
| STAT371 | 2026 | Bayesian Data Analysis | 18 points | Semester 2 |
| STAT372 | 2026 | Stochastic Modelling | 18 points | Semester 1 |
| STAT399 | 2026 | Special Topic | 18 points | Not offered in 2026 |
| STAT401 | 2026 | Applied Statistical Methods and Models | 20 points | Semester 1 |
| STAT402 | 2026 | Regression Models for Complex Data | 20 points | Semester 2 |
| STAT403 | 2026 | Case Studies in Statistics | 20 points | Semester 2 |
| STAT404 | 2026 | Advanced Statistical Inference | 20 points | Semester 1 |
| STAT405 | 2026 | Probability and Random Processes | 20 points | Semester 1 |
| STAT423 | 2026 | Bayesian Modelling | 20 points | Semester 2 |
| STAT424 | 2026 | Research Design and Methods | 20 points | Not offered in 2026 |
| STAT425 | 2026 | Statistical Learning | 20 points | Semester 2 |
| STAT435 | 2026 | Data Analysis for Bioinformatics | 20 points | Semester 1 |
| STAT441 | 2026 | Topic in Advanced Statistics | 20 points | Semester 2 |
| STAT442 | 2026 | Topic in Advanced Statistics | 20 points | Not offered in 2026 |
| STAT490 | 2026 | Dissertation | 40 points | Full Year |
| STAT498 | 2026 | Special Topic | 20 points | Not offered in 2026 |
| STAT499 | 2026 | Special Topic | 20 points | Not offered in 2026 |
INFO papers
| Paper Code | Year | Title | Points | Teaching period |
|---|---|---|---|---|
| INFO203 | 2026 | Human-Computer Interaction and User Experience | 18 points | Semester 1 |
| INFO204 | 2026 | Introduction to Data Science | 18 points | Semester 2 |
| INFO250 | 2026 | Special Topic | 18 points | Not offered in 2026 |
| INFO302 | 2026 | Information Systems Strategy and Governance | 18 points | Semester 1 |
| INFO304 | 2026 | Advanced Data Science | 18 points | Semester 2 |
| INFO305 | 2026 | Advanced Human-Computer Interaction and Interactive Systems | 18 points | Not offered in 2026 |
| INFO310 | 2026 | Software Project Management | 18 points | Semester 1 |
| INFO350 | 2026 | Topics in Information Science | 18 points | Not offered in 2026 |
| INFO351 | 2026 | Special Topic | 18 points | Not offered in 2026 |
| INFO352 | 2026 | Special Topic | 18 points | Not offered in 2026 |
| INFO353 | 2026 | Special Topic | 18 points | Not offered in 2026 |
| INFO390 | 2026 | Research Topics | 18 points | Not offered in 2026 |
| INFO410 | 2026 | Interactive and Immersive Systems | 20 points | Not offered in 2026 |
| INFO420 | 2026 | Statistical Techniques for Data Science | 20 points | Semester 2 |
| INFO424 | 2026 | Adaptive Business Intelligence | 20 points | Semester 1 |
| INFO451 | 2026 | Special Topic | 20 points | Not offered in 2026 |
| INFO452 | 2026 | Special Topic | 20 points | Not offered in 2026 |
| INFO470 | 2026 | Advanced Topics in Information Science | 20 points | Not offered in 2026 |
| INFO490 | 2026 | Dissertation | 40 points | Full Year |
| INFO501 | 2026 | Applied Project | 40 points | 1st Non standard period (23 February 2026 - 12 February 2027), 2nd Non standard period (13 July 2026 - 2 July 2027) |
| INFO580 | 2026 | Research Project | 40 points | 1st Non standard period (23 February 2026 - 12 February 2027), 2nd Non standard period (13 July 2026 - 2 July 2027), 3rd Non standard period (13 July 2026 - 2 July 2027) |
COSC papers
| Paper Code | Year | Title | Points | Teaching period |
|---|---|---|---|---|
| COSC201 | 2026 | Algorithms and Data Structures | 18 points | Semester 1 |
| COSC202 | 2026 | Software Development | 18 points | Semester 1 |
| COSC203 | 2026 | Web, Databases, and Networks | 18 points | Semester 2 |
| COSC204 | 2026 | Computer Systems | 18 points | Semester 2 |
| COSC301 | 2026 | Network Management and Security | 18 points | Semester 1 |
| COSC312 | 2026 | Cryptography and Security | 18 points | Semester 2 |
| COSC326 | 2026 | Computational Problem Solving | 18 points | Semester 1 |
| COSC341 | 2026 | Theory of Computing | 18 points | Semester 1 |
| COSC342 | 2026 | Visual Computing: Graphics & Vision | 18 points | Semester 1 |
| COSC343 | 2026 | Artificial Intelligence | 18 points | Semester 2 |
| COSC344 | 2026 | Database Theory and Applications | 18 points | Semester 1 |
| COSC345 | 2026 | Software Engineering | 18 points | Semester 2 |
| COSC349 | 2026 | Cloud Computing Architecture | 18 points | Semester 2 |
| COSC360 | 2026 | Computer Game Design | 18 points | Summer School |
| COSC385 | 2026 | Research Project | 18 points | Summer School, Semester 1, Semester 2 |
| COSC402 | 2026 | Advanced Computer Networks | 20 points | Semester 2 |
| COSC412 | 2026 | Advanced Cryptography and Security | 20 points | Semester 2 |
| COSC440 | 2026 | Advanced Operating Systems | 20 points | Semester 2 |
| COSC470 | 2026 | Special Topic | 20 points | Not offered in 2026 |
| COSC471 | 2026 | Approved Special Paper | 20 points | Not offered in 2026 |
COMP papers
| Paper Code | Year | Title | Points | Teaching period |
|---|---|---|---|---|
| COMP101 | 2026 | Foundations of Information Systems | 18 points | Summer School |
| COMP121 | 2026 | Computer Programming | 18 points | Semester 1, Semester 2 |
| COMP122 | 2026 | Foundations of Computer Science | 18 points | Summer School, Semester 2 |
| COMP210 | 2026 | Information Assurance | 18 points | Semester 2 |
| COMP421 | 2026 | Machine Learning and Data Mining | 20 points | Semester 2 |
| COMP422 | 2026 | Applied Artificial Intelligence | 20 points | Semester 2 |
| COMP423 | 2026 | Deep Learning | 20 points | Semester 1 |
| COMP424 | 2026 | Information Retrieval and Natural Language Processing | 20 points | Semester 1 |
| COMP425 | 2026 | Advanced Visual Computing | 20 points | Semester 1 |
| COMP427 | 2026 | Agent-based Software Technologies | 20 points | Semester 2 |
| COMP480 | 2026 | Applied Project | 40 points | Full Year |
| COMP490 | 2026 | Dissertation | 40 points | Full Year |
| DATA101 | 2026 | Data Processing and Visualisation | 18 points | Semester 1 |
| DATA403 | 2026 | Data Management | 20 points | Semester 1 |
| DATA404 | 2026 | Management of Large-Scale Data | 20 points | Semester 1 |
More information
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Regulations on this page are taken from the 2026 Calendar and supplementary material.
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