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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 First Semester
Domestic Tuition Fees (NZD) $1,333.93
International Tuition Fees (NZD) $5,793.66

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

Main topics:

  • Linear and logistic regression
  • Models for count data
  • Multivariate analysis
  • Design of experiments
  • Principal components analysis
  • Penalised methods
  • Clustering
Textbooks
Text books 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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Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
A1 Monday 13:00-13:50 9-12, 19-22
Wednesday 13:00-13:50 9-12, 18-22
Friday 13:00-13:50 9-12, 18-22

Tutorial

Stream Days Times Weeks
Attend
A1 Wednesday 14:00-14:50 9-12, 18-21

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 First Semester
Domestic Tuition Fees Tuition Fees for 2021 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
STAT 110
Restriction
STAT 210
Limited to
MBusDataSc, BCom(Hons), BSc(Hons), BA(Hons), PGDipCom, PGDipSci, PGDipArts, BAppSc(Hons), MAppSc, MSc, MBus, PGCertAppSc, PGDipAppSc
Eligibility
Students studying for the MBusDataSc; any student interested in techniques that can be used to model a very broad range of datasets.
Contact
maths@otago.ac.nz
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

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.

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
A1 Monday 13:00-13:50 9-13, 15-16, 18-22
Wednesday 13:00-13:50 9-13, 15-22
Friday 13:00-13:50 9-12, 15-22

Tutorial

Stream Days Times Weeks
Attend
A1 Wednesday 14:00-14:50 9-13, 15-21