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STAT442 Topic in Advanced Statistics

Details available from the Department of Mathematics and Statistics.

This paper provides an overview of ideas and methods that are useful when analysing big data.

Paper title Topic in Advanced Statistics
Paper code STAT442
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period Not offered in 2023 (On campus)
Domestic Tuition Fees (NZD) $1,206.91
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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Students should have completed a first-year paper in statistics (STAT110, STAT115 or BSNS102) and two further papers at 200/300-level that include experience in quantitative research methods or applied statistics before enrolling in STAT442. Students should see the course co-ordinator for approval.

Students should see the Course Co-ordinator for approval. The prerequisite conditions at second-year Statistics may not be compulsory for students majoring in Information Science because the paper content may complement the topics covered in such a major.

Enrolments for this paper require departmental permission.
View more information about departmental permission.


Teaching staff

The paper will be taught by academic staff from the University of Canterbury and the University of Otago.

The course will be delivered by lectures using videoconferencing technology and in-person lectures on the Dunedin campus.

Students have a local contact person/co-ordinator.

Paper Structure


  • Sources and characteristics of big data
  • Challenges with big data
  • Data acquisition, storage and retrieval
  • Data management, cleaning and pre-processing
  • Data visualisation
  • Machine learning methods for high-dimensional data
Teaching Arrangements

Twelve 2-hour lectures.


Textbooks are not required for this paper.

Graduate Attributes Emphasised
Communication, Information literacy, Research.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will develop an ability to analyse a very large dataset and to communicate the information obtained from the analysis.

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Not offered in 2023

Teaching method
This paper is taught On Campus
Learning management system