Data Analytics (2020)

Data Analytics is the science of collecting and interpreting data subject to uncertainty. We live in a world where variability is everywhere. To make informed decisions we must understand the nature of variability, and make use of meaningful information. Without data we have to resort to hunches or guesses, neither of which can be relied on. Data Analytics tells us how to deal with variability, and how to collect and use data so that we can make good decisions.

Data Analytics is available as a first major for the Bachelor of Computing and Mathematical Sciences with Honours and the Bachelor of Science. Data Analytics may also be taken as a second major, subject to academic approval of the Faculty in which the student is enrolled.

To complete Data Analytics as a single major for the BSc or BCMS(Hons), students must gain 135 points from papers listed for Data Analytics, including 105 points above 100 level, and 60 points above 200 level. Students must complete either STATS111 or STATS121, COMPX101, STATS221, STATS226, COMPX223, and either STATS321 or COMPX305. Students in the BCMS(Hons) will also need to take at least 60 points in the subject of Data Analytics at 500 level, including STATS520.

To complete Data Analytics as part of a double major for the BCMS(Hons), BSc or other undergraduate degree, students must gain 120 points from papers listed for Data Analytics, including 90 points above 100 level, and 45 points above 200 level. Students must complete either STATS111 or STATS121, COMPX101, STATS221, STATS226, COMPX223, and either STATS321 or COMPX305. Students in the BCMS(Hons) will also need to take at least 60 points in the subject of their first major at 500 level, including STATS520 if Data Analytics is the first major.

To complete a minor in Data Analytics, students must complete 60 points from the papers listed for the Data Analytics major, including at least 30 points at 200 level or above consisting of at least one STATS-coded paper and at least one COMPX-coded paper.

Note: Students who commenced a major in Statistics in 2017 or prior are encouraged to contact the Faculty of Computing and Mathematical Sciences for programme advice.


On this page


  • Prescriptions for the GradCert(DataA) and GradDip(DataA)

    A Graduate Certificate and Graduate Diploma are available to graduates who have not included Data Analytics at an advanced level in their first degree.

    For further details, contact the Faculty of Computing and Mathematical Sciences Office.

  • 100 Level

    Code Paper Title Occurrence / Location
    COMPX101Introduction to Programming20A (Hamilton), 20A (Online), 20B (Hamilton), 20G (Hamilton) & 20X (Zhejiang University City College, Hangzhou China)
    This paper introduces computer programming in C# - the exciting challenge of creating software and designing artificial worlds within the computer. It also covers concepts such as the internals of the home computer, the history and future of computers, cyber security, computer gaming, databases, mobile computing and current researc...
    CSMAX170Foundations in Computing and Mathematical Sciences20A (Hamilton), 20A (Tauranga) & 20B (Hamilton)
    The objective of this paper is to provide students with the academic foundations for computing and mathematical sciences. The paper will cover the following areas: - Effective academic reasoning and communication - Information literacy and research skills - Academic integrity - Techniques and tools in the computing and mathematical...
    STATS111Statistics for Science20B (Hamilton) & 20B (Tauranga)
    An introductory paper in statistics that uses Microsoft Excel. Topics include the collection and presentation of data, basic principles of experimental design, hypothesis testing, regression and the analysis of categorical data.
    STATS121Introduction to Statistical Methods20A (Hamilton)
    An introduction to statistical data collection and analysis. Topics include general principles for statistical problem solving; some practical examples of statistical inference; and the study of relationships between variables using regression analysis.
  • 200 Level

    Code Paper Title Occurrence / Location
    COMPX223Database Practice and Experience20A (Hamilton)
    This paper approaches the subject of databases from a practical perspective - how do I create a database and how do I retrieve/update data. Both aspects are heavily addressed in this paper. Database creation and querying, using SQL, will be introduced in lectures as you will master practical skills associated with a commercial Data...
    CSMAX270Cultural Perspectives for Computing and Mathematical Sciences20B (Hamilton)
    The paper provides students with an understanding of scientific and culture-specific perspectives on computing and mathematical science issues and the ability to apply these in diverse contexts.
    STATS221Statistical Data Analysis20A (Hamilton)
    This paper introduces students to the R programming language which is used to investigate a collection of real data sets. Analysis of variance, multiple regression, non parametric methods and time series are covered.
    STATS226Bayesian Statistics20B (Hamilton)
    This paper introduces statistical methods from a Bayesian perspective, which gives a coherent approach to the problem of revising beliefs given relevant data. It is particularly relevant for data analytics, statistics, mathematics and computer science.
  • 300 Level

    Code Paper Title Occurrence / Location
    COMPX305Practical Data Mining20B (Hamilton) & 20B (Tauranga)
    This paper introduces students to techniques for automatically finding and exploiting patterns in datasets, covering basic techniques applied in data analytics, data mining, machine learning, and big data. The well-known, locally-made Weka software will be used as the software environment for this paper.
    STATS321Advanced Data Analysis20B (Hamilton)
    This paper covers the use of statistical packages for data analysis and modelling. The emphasis is on observational rather than experimental data. The topics covered are regression modelling and its generalisations, and multivariate analysis.
    STATS322Probability and Mathematical Statistics20A (Hamilton)
    This paper introduces students to probability theory and the mathematical theory of statistics. It covers formally the theoretical foundations of probability, random variables, likelihood and estimation, statistics, and statistical inference.
    STATS323Design and Analysis of Experiments and Surveys20A (Hamilton)
    This paper outlines the principles and practicalities of designing and analysing experiments and surveys, with emphasis on the design.
    STATS326Computational Bayesian Statistics20B (Hamilton)
    Bayesian approach has the potential to model any complex real life problem. In practice, Bayesian methods are implemented using various computational algorithms. This paper introduces the basics of some of the most widely used computational methods, viz the ABC method and the MCMC methods.
    STATS390Directed Study20A (Hamilton) & 20B (Hamilton)
    Students carry out an independent research project on an approved topic under staff supervision.
    STATS391Undergraduate Research Project20A (Hamilton), 20B (Hamilton), 20D (Hamilton) & 20X (Hamilton)
    Students carry out an independent research project on an approved topic under staff supervision.
    STATS397Work-Integrated Learning Directed Study20X (Hamilton)
    Students carry out an independent work-related project on an approved topic under staff supervision.
  • 500 Level

    Code Paper Title Occurrence / Location
    STATS505Optimization20B (Hamilton)
    This paper teaches students a toolbox of optimization techniques. It covers traditional approaches, such as linear programming and Newton's method, and heuristic methods such as simulated annealing and evolutionary algorithms.
    STATS511Computational Statistical Methods20A (Hamilton)
    Computationally intensive statistical methods will be considered. This could include Bayesian methods, methods for high-dimensional data, Big Data, etc. The choice of methods taught will be decided by the lecturer/paper convenor in charge.
    STATS520Dissertation20X (Hamilton)
    A directed investigation and report on an approved project or study topic.
    STATS522Statistical Inference20B (Hamilton)
    Statistical inference will be considered from both the classical and Bayesian perspectives.
    STATS525Topics in Statistics20A (Hamilton)
    This paper will discuss advanced topics in statistics. The exact topics covered could change subject to the preference and research expertise of the academic staff. Students preferences may also be taken into account.
    STATS590Directed Study20B (Hamilton)
    Students have the opportunity to pursue a topic of their own interest under the guidance of academic staff.
    STATS591Dissertation20X (Hamilton)
    A report on the findings of a theoretical or empirical investigation.
    STATS592Dissertation20X (Hamilton)
    A report on the findings of a theoretical or empirical investigation.
    STATS593Statistics Thesis20X (Hamilton)
    An externally examined piece of written work that reports on the findings of supervised research.
    STATS594Statistics Thesis20X (Hamilton)
    An externally examined piece of written work that reports on the findings of supervised research.

2020 Catalogue of Papers information current as of : 16 August 2019 9:46am

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