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INFO420 Statistical Techniques for Data Science

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Explores a range of statistical techniques for data analysis, from statistical modelling of univariate data to the visualisation of patterns in multivariate data.

An introduction to statistical modelling and multivariate analysis that includes generalised linear models and procedures for analysing patterns in multiple quantitative measurements. The paper combines background theory with practice in applying the methods to real datasets.

Paper title Statistical Techniques for Data Science
Paper code INFO420
Subject Information Science
EFTS 0.1667
Points 20 points
Teaching period(s) Semester 2 (Distance learning)
Semester 2 (On campus)
Domestic Tuition Fees (NZD) $1,371.61
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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STAT 110
STAT 210
Limited to
MBusDataSc, BCom(Hons), BSc(Hons), BA(Hons), PGDipCom, PGDipSci, PGDipArts, BAppSc(Hons), MAppSc, MSc, MBus, PGCertAppSc, PGDipAppSc
Students studying for the MBusDataSc; any student interested in techniques that can be used to model a very broad range of datasets.
Teaching staff
Dr Matthew Parry
Paper Structure

Main topics:

  • Linear and logistic regression
  • Multivariate analysis
  • Design of experiments
  • Principal components analysis
  • Penalised methods
  • Classification
  • Clustering

Textbooks are not required for this paper.

Graduate Attributes Emphasised
Scholarship, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes.
Learning Outcomes
Demonstrate in-depth knowledge of the central concepts.

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Semester 2

Teaching method
This paper is taught through Distance Learning
Learning management system

Semester 2

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
L1 Monday 11:00-11:50 28, 30, 32, 34, 36, 38, 40
Wednesday 11:00-12:50 28-34, 36-41


Stream Days Times Weeks
T1 Thursday 15:00-16:50 29-34, 36-41