The science of learning from data.
Data is at the core of modern society. We are producing it, collecting it, wrangling it, analysing it, modelling it, understanding it, visualising it, using it on a scale that seemed impossible not so long ago.
Data Science is fundamentally about how we can learn from data and how we can meaningfully use it to improve our world. Studying Data Science leads to opportunities in fields as diverse as banking and biotechnology, entertainment and education, gaming and government, medicine and manufacturing, retail and research.
Data Science is also a broad area of study. At Otago, the Data Science programme pulls together the best that computer science, information science, and statistics have to offer while stressing application and understanding of the impact of Data Science on society.
Why study Data Science?
Data is everywhere and the demand for data scientists is exploding. Data Science is a broad field but it essentially boils down to extracting information from large and complex data sets. Working as a data scientist for an organisation means you will be at the heart of decision-making processes.
Data Science brings together techniques and methods from computer science, information science, and statistics. This means there are opportunities in areas that particularly interest you, whether it is efficient computation, data storage infrastructure, data analysis or applied machine learning. In addition to problem-solving skills that can be applied to many areas, you will gain valuable communication and data visualisation skills.
Background required
Entry into the Bachelor of Applied Science (BAppSc) in Data Science is open to anyone, however taking Digital Technology for NCEA is useful and NCEA Level 3 Mathematics and Statistics is helpful.
Career opportunities
There are opportunities for Data Science graduates at all levels of business, industry, government, and science.
Can I combine Data Science with other subjects?
Yes.
Because Data Science is a major for the Bachelor of Applied Science, you will need a minor or a second major in an approved subject area.
There are a large number of subject areas to choose from in Applied Science, Arts and Music, Science, as well as all Commerce subjects.
You may even choose Computer Science, Information Science or Statistics.
What will I learn?
Data Science brings together techniques and methods from computer science, information science, and statistics to extract insight from large and complex data sets, and to communicate this acquired knowledge through effective modelling and visualisation.
You will learn how to acquire, handle and analyse data to solve problems in a wide variety of areas. You will also learn to think critically and ethically about the increasing role Data Science plays in society.
How will I learn?
The programme is delivered using lectures, tutorials, and practical labs. Assessment is a combination of assignments, projects, presentations, and exams. There will be opportunities to work in groups.
Qualifications
Explore your study options further. Refer to enrolment information found on the following qualification pages.
- Bachelor of Applied Science* (BAppSc)
- Bachelor of Arts and Science (BASc)
- Bachelor of Commerce and Science (BComSc)
- Diploma for Graduates (DipGrad)
*It is a requirement that every Bachelor of Applied Science (BAppSc) normally includes an approved minor subject or an approved second major subject. Usually such a minor or second major subject must be selected from the approved combinations of major subjects with minor or second major subjects. Some exceptions may apply. For details see:
Full list of available approved minor and second major subject areas
Programme requirements
Bachelor of Applied Science (BAppSc) majoring in Data Science
| Year | Papers | Points |
|---|---|---|
| 100-level |
COMP 101 Foundations of Information Systems COMP 120 Practical Data Science COMP 161 Computer Programming COMP 162 Foundations of Computer Science 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 18 18 18 18 |
| 200-level |
COSC 201 Algorithms and Data Structures INFO 204 Introduction to Data Science STAT 210 Applied Statistics STAT 260 Visualisation and Modelling in R |
18 18 18 18 |
| 300-level |
COSC 343 Artificial Intelligence INFO 304 Advanced Data Science STAT 312 Modelling High Dimensional Data One of COSC 344 Database Theory and Applications, STAT 310 Statistical Modelling |
18 18 18 18 |
| Plus |
144 further points, including either requirements for an approved minor or approved second major subject or other approved papers. |
144 |
| Total | 360 |
Papers
COMP papers
| Paper code | Year | Title | Points | Teaching period |
|---|---|---|---|---|
| COMP101 | 2023 | Foundations of Information Systems | 18 points | Semester 2, Summer School |
| COMP111 | 2023 | Information and Communications Technology | 18 points | Semester 2 |
| COMP120 | 2023 | Practical Data Science | 18 points | Semester 1, Semester 2 |
| COMP151 | 2023 | Programming for Scientists | 18 points | Semester 1 |
| COMP161 | 2023 | Computer Programming | 18 points | Semester 1, Semester 2, 1st Non standard period |
| COMP162 | 2023 | Foundations of Computer Science | 18 points | Semester 2, Summer School |
| COMP210 | 2023 | Information Assurance | 18 points | Semester 2 |
| COMP270 | 2023 | ICT Fundamentals | 15 points | Not offered in 2023 |
| COMP371 | 2023 | ICT Studio 1 | 15 points | Not offered in 2023 |
| COMP372 | 2023 | ICT Studio 2 | 15 points | Not offered in 2023 |
| COMP373 | 2023 | ICT Studio 3 | 15 points | Not offered in 2023 |
| COMP390 | 2023 | ICT Industry Project | 30 points | 1st Non standard period, 2nd Non standard period, 3rd Non standard period |
COSC papers
INFO papers
STAT papers
| Paper code | Year | Title | Points | Teaching period |
|---|---|---|---|---|
| STAT110 | 2023 | Statistical Methods | 18 points | Semester 1, Summer School |
| STAT115 | 2023 | Introduction to Biostatistics | 18 points | Semester 2 |
| STAT210 | 2023 | Applied Statistics | 18 points | Semester 1 |
| STAT260 | 2023 | Visualisation and Modelling in R | 18 points | Semester 2 |
| STAT270 | 2023 | Probability and Inference | 18 points | Semester 1 |
| STAT310 | 2023 | Statistical Modelling | 18 points | Semester 1 |
| STAT311 | 2023 | Design of Research Studies | 18 points | Semester 1 |
| STAT312 | 2023 | Modelling High Dimensional Data | 18 points | Semester 2 |
| STAT370 | 2023 | Statistical Inference | 18 points | Semester 2 |
| STAT371 | 2023 | Bayesian Data Analysis | 18 points | Semester 2 |
| STAT372 | 2023 | Stochastic Modelling | 18 points | Semester 1 |
| STAT399 | 2023 | Special Topic | 18 points | Not offered in 2023 |
| STAT401 | 2023 | Applied Statistical Methods and Models | 20 points | Semester 1 |
| STAT402 | 2023 | Regression Models for Complex Data | 20 points | Semester 2 |
| STAT403 | 2023 | Case Studies in Statistics | 20 points | Semester 2 |
| STAT404 | 2023 | Advanced Statistical Inference | 20 points | Semester 1 |
| STAT405 | 2023 | Probability and Random Processes | 20 points | Semester 1 |
| STAT423 | 2023 | Bayesian Modelling | 20 points | Semester 2 |
| STAT424 | 2023 | Research Design and Methods | 20 points | Semester 1 |
| STAT425 | 2023 | Statistical Learning | 20 points | Semester 2 |
| STAT435 | 2023 | Data Analysis for Bioinformatics | 20 points | Semester 1 |
| STAT441 | 2023 | Topic in Advanced Statistics | 20 points | Semester 2 |
| STAT442 | 2023 | Topic in Advanced Statistics | 20 points | Not offered in 2023 |
| STAT490 | 2023 | Dissertation | 40 points | Full Year, 1st Non standard period |
| STAT498 | 2023 | Special Topic | 20 points | Not offered in 2023 |
| STAT499 | 2023 | Special Topic: Clinical Trials | 20 points | Not offered in 2023 |
Key information for future students
Contact us
Matthew Parry
Department of Mathematics and Statistics
Tel +64 3 479 7780
Email matthew.parry@otago.ac.nz
Grant Dick
Department of Information Science
Tel +64 3 479 8180
Email grant.dick@otago.ac.nz
Brendan McCane
Department of Computer Science
Tel +64 3 479 8588
Email brendan.mccane@otago.ac.nz
