Bachelor of Data Science

BDataSc*

Data science is one of the world’s fastest growing professions. The Bachelor of Data Science degree (BDataSc) will prepare you to excel in our data-focused world.

Overview

Data science is a massive emerging growth area globally and considered one of the essential skills of the 21st century.

The Bachelor of Data Science takes you deep into the world of ‘big data’ – the vast amounts of data generated and collected by people and organisations every second. You’ll learn how to analyse past and current data; reveal patterns and trends on a large scale; and provide predictions and insights into everything from social behaviours to the natural environment.  

Be prepared with the knowledge and skills employers are looking for in a data-focused world.

*subject to Te Pōkai Tara | Universities New Zealand CUAP approval

Study data science at UC and you will:

  • Learn from highly regarded experts in biology, computing, data science, geography, linguistics, mathematics and more.
  • We collaborate with a range of specialist, internationally recognised organisations working in the data science.
  • Learn and study in a new state-of-the-art regional research centre with high-tech computing systems and technology.
  • This is a practical, work-focused degree developed in response to industry demand, with input from employers.

The BDataSc degree contains a core of maths, data science and computer science. Along with these core subjects, students choose a ‘major’ subject to specialise in. We offer five majors in the BDataSc:

Bioinformatics. Use a wide range of applications and tools to understand and manage the vast amounts of complex biological data generated from scientific research.

Computational Linguistics. Apply computer science to the analysis, synthesis and comprehension of written and spoken language. Used in everything from speech recognition systems to search engines.

Data Science. Analyse past and current data to provide predictions and valuable insights into everything from social behaviours to the natural environment.

Population Health Data Science. Find data-driven solutions to disease prevention and improve public health and well-being on a large scale.

Spatial Data Science. Use location-based data and tools like Geographic Information Systems to find patterns and tackle complex problems.

Year 1

 
DATA101
 
 
 
MAJOR
 
MAJOR
 
ELECTIVE
 
Year 2
 
 
 
 
MAJOR
 
MAJOR
 
ELECTIVE
 
Year 3
 
DATA303
 
MAJOR
 
MAJOR
 
MAJOR (30 point project)
 
ELECTIVE
 
  •  
    Required courses for Bachelor of Data Science
  •  
    Major subject courses for Bachelor of Data Science
  •  
    Courses from Data Science or other degrees
(1) SCIE 101 is a compulsory course for all BDataSc students.
Each small block represents a 15-point course. However, some courses may be 30 points (or more).

Bioinformatics

Gaining insight into biological data

A major in Bioinformatics will provide graduates with the skills to understand and apply data science techniques and concepts to their professional practice, contributing to positive outcomes in large-scale biological data analysis. Graduates will possess a rounded understanding of the analysis of genomic data in a biological context, including the analysis, interpretation, integration and management of biological data; the application of existing and the generation of novel computational tools for analysis of large-scale biological data; and the integration of big data into biological research programs in a statistically-informed manner.

Graduates will possess specific knowledge relating to a range of common issues, including:

  • The ability to adapt and apply existing bioinformatics methods
  • Advanced programming/coding skills across multiple programming languages (Linux, Bash, R, Shell, Git, Java, Python, Perl)
  • The ability to use and apply simulated datasets to answer questions in biology
  • An understanding of genetics, molecular biology and statistics

Typical degree structure

Year 1

 
DATA101
 
 
 
 
 
ELECTIVE
 
Year 2 Year 3
 
DATA303
 
 
 
BIOL338 (30 point project)
 
BIOL337
 
  •  
    Required courses for Bachelor of Data Science
  •  
    Bioinformatics major subject courses for Bachelor of Data Science
  •  
    Courses from Data Science or other degrees
(1) SCIE 101 is a compulsory course for all BDataSc students.
Each small block represents a 15-point course. However, some courses may be 30 points (or more).

Recommended courses for the Bioinformatics major include:

  • 45 points at 100 level, comprising BIOL111, BIOL112, and one 15-points elective (Schedule S)
  • 45 points at 200 level, comprising BIOL215, BIOL231, BIOL271
  • 75 points at 300 level, comprising BIOL333, BIOL334, BIOL337, BIOL338

Electives

Electives to be chosen from any undergraduate course at UC.

Computational linguistics

Applying technology to language theory

A major in Computational Linguistics will provide graduates with knowledge of linguistic theory as well as practical and technical skills in language technology. Graduates will have a multi-disciplinary and flexible grounding in linguistics, computer science, and data science. Skills will be developed in both symbolic and quantitative reasoning, together with the ability to formulate and communicate arguments to support evidence-based decision making. The multi-disciplinary background will enable graduates to work effectively in multi-disciplinary teams with ethical training to ensure ethical and legal obligations are satisfied in their future work environments. 

Graduates will gain experience with a range of common issues, including:

  • The ability to adapt linguistic concepts into computational algorithms
  • The ability to evaluate the assumptions and performance of computational models
  • Experience using common datasets (web-crawling, social media, Wikipedia, etc.)
  • Experience working across languages relevant to Aotearoa New Zealand (Māori, English, Tongan, Samoan, Chinese, Japanese)
  • Experience implementing proto-types in Python

Typical degree structure

Year 1

 
DATA101
 
 
 
 
 
ELECTIVE
 
Year 2
 
 
 
 
 
 
ELECTIVE
 
Year 3
 
DATA303
 
 
LING315
 
LING310 (30 point project)
 
ELECTIVE
 
  •  
    Required courses for Bachelor of Data Science
  •  
    Computational Linguistics Major subject courses for Bachelor of Data Science
  •  
    Courses from Data Science or other degrees
(1) SCIE 101 is a compulsory course for all BDataSc students.
Each small block represents a 15-point course. However, some courses may be 30 points (or more).

Recommended courses for the Computational Linguistics major include:

  • 45 points at 100 level, comprising LING 101, LING 102; and one 15-points elective (Schedule S)
  • 45 points at 200 level, comprising LING 217, LING 223; and one 15-points elective (Schedule S)
  • 75 points at 300 level, comprising LING 310, LING315, COSC 367; and one 15-points elective (Schedule S)

Electives

Electives to be chosen from:

  • Any Ethics course
  • Other LING or DIGI courses
  • COSC 261
  • Any 200-level DATA course
  • Any 200-level STAT course
  • Any 300-level DATA course
  • Any 300-level STAT course
  • Any 300-level COSC course

Data Science

Delving into big and small data

Graduates of the Data Science major will develop a valuable set of skills that include problem solving, critical thinking, numerical confidence, advanced analytical capability, communication, group work, design and programming database software. This major will provide graduates will an understanding of techniques and theories from mathematics, statistics, computer science and artificial intelligence.

Graduates will have experience with a range of common issues, including:

  • Critical evaluation of findings and discussions in data science literature
  • Strategies in communication and visualisation of findings from data
  • Experience advanced programming/coding skills across multiple programming languages (R, Python)
  • Engage in rigorous intellectual analysis, criticism and problem-solving
  • Work successfully with others and with a community/industry partner

Typical degree structure

Year 1

 
DATA101
 
 
 
ELECTIVE
 
ELECTIVE
 
ELECTIVE
 
Year 2
 
 
 
 
 
One from STAT211–
299
 
ELECTIVE
 
Year 3
 
DATA303
 
 
 
DATA309
 
ELECTIVE
 
  •  
    Required courses for Bachelor of Data Science
  •  
    Data Science Major subject courses for Bachelor of Data Science
  •  
    Courses from Data Science or other degrees
(1) SCIE 101 is a compulsory course for all BDataSc students.
Each small block represents a 15-point course. However, some courses may be 30 points (or more).

Recommended courses for the Data Science major include:

  • 45 points of electives at 100 level (Schedule S)
  • 45 points at 200 level, comprising COSC 265, 1 course from STAT211-299, and one 15-points elective (Schedule S)
  • 75 points at 300 level, comprising COSC 367, STAT315, DATA309, and one 15-point elective (Schedule S); NOTE: students must complete both  STAT315 and STAT318

Electives

100-level electives: Any 100-level course at UC

All remaining electives to be chosen from:

  • Any 200-level course at UC
  • Any 300-level COSC course
  • Any 300-level DATA course
  • Any 300-level MATH course
  • Any 300-level SENG course
  • Any 300-level STAT course

Population Health Data Science

Improving health outcomes through data

Graduates of the Population Health Data Science major will gain knowledge and skills in science and health, experience in critical appraisal and scientific investigation, and an understanding of values and ethics in health. Graduates will have the ability to apply these skills to improve health and well-being through disease prevention using data-driven solutions. The major in Population Health Data Science aims to provide students with a strong foundation in health sciences, with detailed knowledge in population health using data science methodologies. Students will be prepared for career opportunities in the health sector by gaining practical experience in working as part of multidisciplinary teams.

Graduates will possess specific knowledge relating to a range of common issues, including:

  • The ability to adapt and apply existing health informatics methods
  • Advanced programming/coding skills across multiple programming languages
  • Generation and interpretation of artificial intelligence and machine learning, image processing, and geospatial data applied to public health and epidemiology
  • The ability to use and apply simulated datasets to answer questions in epidemiology and clinical or public health applications

Typical degree structure

Year 1

 
DATA101
 
 
 
 
GISC101
 
ELECTIVE
 
Year 2
 
 
 
 
 
 
ELECTIVE
 
Year 3
 
DATA303
 
 
 
HLTH309
 
ELECTIVE
 
  •  
    Required courses for Bachelor of Data Science
  •  
    Population Health Data Science major subject courses for Bachelor of Data Science
  •  
    Courses from Data Science or other degrees
(1) SCIE 101 is a compulsory course for all BDataSc students.
Each small block represents a 15-point course. However, some courses may be 30 points (or more).

Recommended courses for the Population Health Data Science major include:

  • 45 points at 100 level, comprising HLTH110, GISC101, and one 15-point elective (Schedule S)
  • 45 points at 200 level, comprising HLTH213, HLTH214, and one 15-point elective (Schedule S)
  • 75 points at 300 level, comprising GEOG325, HLTH312, HLTH309, and one 15-point elective (Schedule S)

Electives

100-level elective options:

200-level elective options:

300-level elective options:

Spatial Data Science

The science of 'where'

Graduates of the Spatial Data Science major will be able to analyse and extract deeper insight from spatial data using a comprehensive set of analytical methods and spatial algorithms, including machine learning and spatial statistics.  Graduates will be able to apply spatial data science and spatial thinking to uncover hidden patterns and improve predictive modelling. They will gain experience working with powerful analytical tools in R, Python, and ArcGIS software and learn how to integrate popular open data science packages into their analyses.

Graduates majoring in Spatial Data Science will possess a rounded understanding of the collection, analysis and interpretation of spatial data (data with geographic attributes), and will:

  • Understand the nature of spatial data
  • Be competent in spatial thinking and reasoning
  • Be able to analyse computational tools for analysis of large-scale spatial data
  • Be able to contribute to spatial data-driven research programs, in a statistically-informed manner
  • Have advanced programming/coding skills across multiple programming languages (Python, R)
  • Be able to use and apply datasets to answer a range of geographic questions generated by spatial data

Typical degree structure

Year 1

 
DATA101
 
 
 
GISC101
 
ELECTIVE
 
Year 2
 
 
 
 
 
 
ELECTIVE
 
Year 3
 
DATA303
 
 
 
GISC309
 
ELECTIVE
 
  •  
    Required courses for Bachelor of Data Science
  •  
    Spatial Data Science major subject courses for Bachelor of Data Science
  •  
    Courses from Data Science or other degrees
(1) SCIE 101 is a compulsory course for all BDataSc students.
Each small block represents a 15-point course. However, some courses may be 30 points (or more).

Recommended courses for the Spatial Data Science major include:

  • 45 points at 100 level, comprising GEOG106 or GEOG110, GISC101, and one 15-point elective (Schedule S)
  • 45 points at 200 level, comprising GEOG205, GEOG208, and one 15-point elective (Schedule S)
  • 75 points at 300 level, comprising GEOG323, GEOG324, GISC309, and one 15-point elective (Schedule S)

Electives

100-level elective options:

ENVR101

200-level elective options:

300-level elective options:

As a graduate of BDataSc, you can develop your independent research skills, or boost your career and earning potential with postgraduate qualifications in data science or other specialist fields, including:

There’s a growing demand for data scientists across all employment areas, driven by the exponential growth of data and a desire by industry and governments to use that data for better outcomes. As organisations all over the world recognise the value of ‘Big Data’ and what it can reveal, they’re increasingly needing people who can make sense of it.

A range of career options are available for graduates in the following areas:

  • technology, finance or insurance companies
  • research and science organisations
  • healthcare and medical sector
  • start-up and consulting businesses
  • government agencies

Interested in studying towards a Bachelor of Data Science?