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Contents

Quick Facts

Programme Tabs

Overview

Programme Overview

The ability to turn data into information, knowledge and innovative products is a skill in high demand within industry. By completing a strong core of Computer Science and Statistics courses, you will gain a unique combination of skills in Data Science, enabling you to comprehend, process and manage data effectively to extract value from it. Graduates will be critical, reflective practitioners able to pursue professional goals or further postgraduate study.

We also offer the Master of Data Science (MDataSci) as a 240-point taught masters as a March intake only. This is suitable for students who have a background in either Computer Science or Statistics, but not both. 

Students who have majored in Data Science, or a combination of Computer Science and Statistics, should apply for the 180-point taught masters. 

Programme Careers

Potential Careers

With the current demand for continued professional development in this area, this advanced qualification will help you to develop your data science skills to become well-positioned to pursue employment in the data science industry.

Jobs related to this programme

  • Business analyst
  • Big data solutions architect
  • Data mining engineer
  • Data scientist
  • Digital product designer
  • Machine learning engineer


Further Study Options

Student career planning services

Once you become a student at the University, you can get help with planning and developing your career from Career Development and Employability Services.

Quick guides to postgraduate Data Science for international students

International guides for Chinese students

International guides for Indian students

Key Information for Students

Key Information for Students

Compare qualifications and academic information across different New Zealand institutions.

Experience the University

Video

Meet the programme director

Learn more about the MDataSci programme from Professor Sebastian Link at the School of Computer Science.

Entry Requirements, Fees and Dates

Programme Entry Requirements

Other qualifications

If your highest qualification was gained from another tertiary institution, view our entry requirements.


University of Auckland minimum programme requirements

Minimum requirements listed here are the likely grades required and do not guarantee entry. We assess each application individually and applicants may require a higher grade to be offered a place. 

Taught (180 point) - 4.0 Grade Point Average 

Bachelor of Science in Data Science or a Bachelor of Science with a major in Computer Science and a major in Statistics

Taught (240 point) - 4.0 Grade Point Average

Bachelor of Science

Calculate your Grade Point Average (GPA)

Postgraduate Requirements

You'll also need to meet other requirements, including time limits and total points limits. See Postgraduate enrolment.

Find a Supervisor

Depending on the type of programme you are pursuing, you may have the opportunity to complete a unique research project. To find out more about the research carried out at the University of Auckland and to identify potential supervisors, check out our researcher profiles.

Other Pathways to Study

Taught 180 points

You must have completed either:

  • A Bachelor of Science in Data Science from this University, with a Grade Point Average of 4.0 or higher in 60 points above Stage II. OR
  • A Bachelor of Science with a major in Computer Science and a major in Statistics from this University, with a Grade Point Average of 4.0 or higher in 60 points above Stage II.

Taught 240 points

You must have completed a Bachelor of Science from this University in a similar field with a GPA of 4.0 in 60 points at Stage lll or above, and passed COMPSCI 130, MATHS 108, and STATS 101, or equivalent prior study. 

If you do not meet the above entry requirements, but have other relevant experience and think you would be successful in postgraduate study, please contact us to discuss alternative pathways into our programmes. 

Fees and scholarships

Fees

Fees Disclaimer

Fees are set in advance of each calendar year and will be updated on this website. Fees are inclusive of 15% GST, but do not include the Student Services Fee, course books, travel and health insurance, or living costs. Fees will be confirmed upon completion of enrolment into courses. For more information, please see Fees and Money Matters.

*Please note: amounts shown are indicative and estimates only.

Scholarships

Scholarships and awards

Find out about the scholarships you may be eligible for.

Loans and Allowances

Student loans and allowances

Are you a New Zealand citizen or resident? You could be eligible for a student loan or allowance.

Cost of Living

Cost of living

Get an idea of how much accommodation and general living in Auckland will cost.

Key Dates

Application Advice

Please note: we will consider late applications if places are still available. International students should start the application process as early as possible to allow sufficient time to apply for a visa.

Key Dates

SemesterApplication Closing DateSemester Start DateSemester End Date
2026 Semester One08 December 202502 March 202629 June 2026
2026 Semester Two08 June 202620 July 202616 November 2026

Other Important Dates

See important dates for the academic year, including orientation, enrolment, study breaks, exams, and graduation.

Additional Information on Key Dates

The Late Year term entry is only available for the 240 point programme.

Regulations

Preamble

The regulations for this degree are to be read in conjunction with all other relevant statutes and regulations including the Academic Statutes and Regulations.

Admission

1 In order to be admitted to this degree, an applicant intending to complete 180 points must have:

a completed the requirements for the Bachelor of Science from this University with Grade Point Average of 4.0 or higher, and a specialisation in Data Science, or have equivalent prior study

or

b completed the requirements for the Bachelor of Science from this University with a Grade Point Average of 4.0 or higher in 60 points above Stage II, and a specialisation in Data Science

or

c completed the requirements for the Bachelor of Science from this University with a Grade Point Average of 4.0 or higher, and a major in both Computer Science and Statistics, or have equivalent prior study

or

d completed the requirements for the Bachelor of Science from this University with a Grade Point Average of 4.0 or higher in 60 points above Stage II, and a major in both Computer Science and Statistics

or

e completed the requirements for the Postgraduate Certificate in Data Science from this University with a Grade Point Average of 4.0 or higher.

2 In order to be admitted to this degree, an applicant intending to complete 240 points must have:

a (i) completed the requirements for the Bachelor of Science from this University with a programme Grade Point Average of 4.0 or higher, and a major in Computer Science or Statistics, or have equivalent prior study

and

(ii) passed COMPSCI 130, MATHS 108, and STATS 101 or equivalent courses

or

b (i) completed the requirements for the Bachelor of Science from this University with a Grade Point Average of 4.0 or higher in 60 points above Stage II, and a major in Computer Science or Statistics

and

(ii) passed COMPSCI 130, MATHS 108, and STATS 101 or equivalent courses

or

c passed 60 points towards the Postgraduate Certificate in Data Science from this University with a Grade Point Average or 4.0 or higher, provided that the postgraduate certificate has not been awarded.

3 Equivalence in Regulation 1 will be determined by the University. Equivalence pertains to the standard as well as nature and level of study.

4 In exceptional circumstances, the requirement in Regulation 1 or 2 may be waived by the relevant Associate Dean Academic or nominee if they determine that an applicant has at least three years of relevant practical, professional or scholarly experience that provides an equivalent level of preparation.

Duration and Total Points Value

5 A student admitted to this degree under Regulation 1 or 4 must:

a pass courses with a total value of 180 points

and

b complete within the time limit specified in the General Regulations – Masters Degrees

and

c not exceed 220 points for the total enrolment in this degree.

6 A student admitted to this degree under Regulation 2 or 4 must:

a pass courses with a total value of 240 points

and

b complete within the time limit specified in the General Regulations – Masters Degrees

and

c not exceed 280 points for the total enrolment in this degree.

Structure and Content

7 a A student enrolled for this degree must complete the requirements as listed in the Master of Data Science Schedule.

b A student who has to complete 180 points must achieve a Grade Point Average of 4.0 or higher in the first 60 points of taught courses prior to enrolment in DATASCI 791. If this Grade Point Average is not achieved, enrolment in the Master of Data Science cannot continue.

c A student who has to complete 240 points must achieve a Grade Point Average of 4.0 or higher in the first 120 points of taught courses prior to enrolment in DATASCI 791. If this Grade Point Average is not achieved, enrolment in the Master of Data Science cannot continue.

8 A student must complete the University of Auckland Academic Integrity course as specified in the Enrolment and Programme Regulations, Academic Integrity, of the University Calendar.

Research Component

9 a The dissertation is to be carried out under the guidance of a supervisor appointed by the Academic Head.

b The dissertation topic must be approved by the Programme Director prior to enrolment.

c The dissertation is to be completed and submitted in accordance with the General Regulations – Masters Degrees.

Credit, Cross-credit and Reassignment

10 A student who does not achieve the Grade Point Average specified in Regulation 7 may apply to reassign courses passed for this degree to the Postgraduate Diploma in Science or the Postgraduate Certificate in Data Science.

Distinction

11 This degree may be awarded with Honours in accordance with the General Regulations – Masters Degrees.

Variations

12 In exceptional circumstances the Programme Director may approve a variation to a student’s programme of study in accordance with the Enrolment and Programme Regulations.

Regulation Commencement or Amendment

13 These regulations and/or schedule have been amended with effect from 1 January 2025.

Schedule

Programme Schedules

Complete 180 points comprising:

  • Either:
  • 1 item from 180 points Requirements, or
  • 1 item from 240 points Requirements, and
  • 1 item from Academic Integrity Course

180 points Requirements

Complete exactly 1 of the following:

240 points Requirements

Complete exactly 1 of the following:

Academic Integrity Course

Complete exactly 1 of the following:

Degree Plan

Overview

DATASCI 791 Research Project must be taken full-time in the final semester of your programme.


Please note this is a sample structure only.

Degree Plan for the Master of Data Science - 240 points

Year 1
Semester One
Computational Introduction to Statistics
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 1 Elective Courses
15 Points
Big Data Management
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Compulsory Courses
15 Points
Foundations of Machine Learning
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 1 Elective Courses
15 Points
Digital Innovation
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 4 Elective Courses
15 Points
Academic Integrity Course
Academic Integrity Course
Points
Semester Two
Algorithms for Massive Data
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 3 Elective Courses
15 Points
Data Mining and Big Data
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 4 Elective Courses
15 Points
Advanced Topics in Machine Learning
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Compulsory Courses
15 Points
Statistical Learning for Data Science
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 1 Elective Courses
15 Points
Year 2
Semester One
Statistical Computing Skills for Professional Data Scientists - Level 9
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Statistical Computing Skills for Professional Data Scientists Course
15 Points
Regression for Data Science
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Statistics Courses
15 Points
Statistical Computing
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 1 Elective Courses
15 Points
Data Visualisation
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 2 Elective Courses
15 Points
Semester Two
Advanced Data Science Practice
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Compulsory Courses
15 Points
Advanced Topics in Artificial Intelligence
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Group 4 Elective Courses
15 Points
Research Project - Level 9
240 points Requirements - DATA-T4MDataSci - Master of Data Science specialisation in Data Science - Taught 240 - Research Project - 1 Semester
30 Points

Graduate Profile and Programme Capabilities

Graduate Profile

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Programme Capabilities

#Programme Capability

Programme Capabilities to Graduate Profile Map