Data Analytics (2022)

Data Analytics is available as a specified programme for the Bachelor of Computer Science (BCompSc) - see the Prescriptions for the BCompSc.

Data Analytics is available as a first major for the Bachelor of Climate Change (BCC), Bachelor of Computing and Mathematical Sciences with Honours (BCMS(Hons)) and the Bachelor of Science (BSc). Data Analytics may also be taken as a second major or minor, subject to approval of the Division in which the student is enrolled.

To complete Data Analytics as a single major for the BCC, BCMS(Hons) or the BSc, 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 COMPX101 or ENGEN103, STATS111 or STATS121, COMPX223, STATS221, STATS222, and STATS321. 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 BCC, 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 COMPX101 or ENGEN103, STATS111 or STATS121, COMPX223, STATS221, STATS222, and STATS321. 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.

On this page

• Prescriptions for the BCompSc
• Prescriptions for the GradCert(DataA) and GradDip(DataA)
• 100 Level
• 200 Level
• 300 Level
• 500 Level

• Prescriptions for the BCompSc

  To complete Data Analytics as a specified programme for the BCompSc, students must take the following papers:

  Year 1: COMPX101, COMPX102, CSMAX170, MATHS135, STATS111 or STATS121, ENGEN101 or MATHS165 or PHILO102, DIGIB101 or MGSYS101, and ENGEN103.

  Year 2: COMPX201 or COMPX241, COMPX202 or COMPX242, COMPX203, COMPX204, COMPX222, COMPX223, CSMAX270 and STATS221.

  Year 3: COMPX301, COMPX324, COMPX361 or COMPX307, either (15 points from any 300 level COMPX and one of COMPX374 or COMPX397) or COMPX398, COMPX205 or STATS321, COMPX310 and 15 points from any 300 level STATS paper.

• 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 Division of Health, Engineering, Computing and Science Office.

• 100 Level

  Code	Paper Title	Points	Occurrence / Location
  COMPX101	Introduction to Programming	15.0	22A (Hamilton), 22A (Online), 22B (Hamilton) & 22X (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...
  CSMAX170	Foundations in Computing and Mathematical Sciences	15.0	22A (Hamilton), 22A (Tauranga) & 22B (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 sci...
  ENGEN103	Engineering Computing	15.0	22A (Hamilton), 22A (Tauranga) & 22G (Hamilton)
  This paper introduces computer programming in languages such as the MATLAB language. It provides the basis for the programming skills required in more advanced papers.
  STATS111	Statistics for Science	15.0	22B (Hamilton) & 22B (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.
  STATS121	Introduction to Statistical Methods	15.0	22A (Hamilton) & 22A (Online)
  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	Points	Occurrence / Location
  COMPX223	Database Practice and Experience	15.0	22A (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...
  CSMAX270	Cultural Perspectives for Computing and Mathematical Sciences	15.0	22B (Hamilton) & 22B (Tauranga)
  The paper provides students with an understanding of scientific and culture-specific perspectives on issues in computing and mathematical sciences. Students will learn how these perspectives can be applied in diverse cultural, international, ethical, and professional contexts.
  STATS221	Statistical Data Analysis	15.0	22A (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.
  STATS222	Principles of Probability and Statistics	15.0	22B (Hamilton)
  This paper introduces the theoretical background that underpins modern probability and statistics. Topics include discrete probability and mathematical statistics from a frequentist and Bayesian viewpoint.

• 300 Level

  Code	Paper Title	Points	Occurrence / Location
  COMPX310	Machine Learning	15.0	22B (Hamilton) & 22B (Tauranga)
  This paper introduces Machine Learning (ML) which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while processing large datasets. This paper will use standard Python-based ML toolkits to teach the fundamentals of ML.
  STATS321	Advanced Data Analysis	15.0	22A (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.
  STATS322	Probability and Stochastic Processes	15.0	22A (Hamilton)
  This paper introduces students to probability theory and stochastic processes. It covers formally the theoretical foundations of probability, random variables, statistics, stochastic processes and Markov chains.
  STATS323	Design and Analysis of Experiments and Surveys	15.0	22A (Hamilton)
  This paper outlines the principles and practicalities of designing and analysing experiments and surveys, with emphasis on the design.
  STATS326	Computational Bayesian Statistics	15.0	No occurrences
  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.
  STATS390	Directed Study	15.0	22A (Hamilton), 22A (Online), 22B (Hamilton) & 22B (Online)
  Students carry out an independent research project on an approved topic under staff supervision.
  STATS391	Undergraduate Research Project	30.0	22A (Hamilton), 22B (Hamilton), 22D (Hamilton) & 22X (Hamilton)
  Students carry out an independent research project on an approved topic under staff supervision.
  STATS397	Work-Integrated Learning Directed Study	15.0	22X (Hamilton)
  Students carry out an independent work-related project on an approved topic under staff supervision.

• 500 Level

  Code	Paper Title	Points	Occurrence / Location
  COMPX521	Machine Learning Algorithms	15.0	22A (Hamilton)
  This paper exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms.
  STATS505	Optimization	15.0	22B (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.
  STATS520	Dissertation	45.0	22X (Hamilton) & 22X (Online)
  A directed investigation and report on an approved project or study topic.
  STATS525	Topics in Statistics	30.0	22A (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.
  STATS590	Directed Study	30.0	22B (Hamilton)
  Students have the opportunity to pursue a topic of their own interest under the guidance of academic staff.
  STATS591	Dissertation	30.0	22X (Hamilton) & 22X (Online)
  A report on the findings of a theoretical or empirical investigation.
  STATS592	Dissertation	60.0	22X (Hamilton) & 22X (Online)
  A report on the findings of a theoretical or empirical investigation.
  STATS593	Statistics Thesis	90.0	22X (Hamilton) & 22X (Online)
  An externally examined piece of written work that reports on the findings of supervised research.
  STATS594	Statistics Thesis	120.0	22X (Hamilton) & 22X (Online)
  An externally examined piece of written work that reports on the findings of supervised research.