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STAT312 Modelling High Dimensional Data

An introduction to the statistical learning techniques commonly used to analyse high-dimensional (or multivariate) data. Penalised regression, classification trees, clustering, dimension-reduction, bagging, stacking, boosting, random forests and ensemble learning.

The aim of this paper is to introduce students to many of the statistical learning techniques that are now used to analyse high-dimensional data. Students will learn the underlying rationale for each method and gain practice.

Paper title Modelling High Dimensional Data
Paper code STAT312
Subject Statistics
EFTS 0.15
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $955.05
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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One of (ECON 210 or FINC 203 or STAT 210 or STAT 241) and STAT 260
STAT 242, STAT 342, STAT 425
Schedule C
Arts and Music, Science

Teaching staff

Dr Matthew Parry

Paper Structure

Main topics

  • Penalised regression
  • Classification trees
  • Clustering
  • Dimension-reduction
  • Bagging, stacking and boosting
  • Random forests
  • Ensemble learning

Textbooks are not required for this paper

Graduate Attributes Emphasised
Lifelong learning, Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.
Learning Outcomes

On completion of this paper, students will be able to:

  1. Describe the issues that arise when analysing high-dimensional data.
  2. Use a range of statistical learning techniques to analyse real, high-dimensional data using R
  3. Determine which is the most appropriate technique for a give research objective
  4. Interpret the results of the analysis, including any assumptions or limitations.
  5. Write a clear and succinct report on an analysis of high-dimensional data collaborator or potential client.

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Semester 2

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
A1 Monday 12:00-12:50 28-34, 36-41
Thursday 12:00-12:50 28-34, 36-41
Friday 13:00-13:50 29-34, 36-41


Stream Days Times Weeks
Attend one stream from
A1 Tuesday 14:00-14:50 28-34, 36-41
A2 Friday 15:00-15:50 29-34, 36-41