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INFO420 Statistical Techniques for Data Science

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 Second Semester
Domestic Tuition Fees (NZD) $1,256.92
International Tuition Fees (NZD) $5,151.03

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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
  • Design of experiments
  • Principal components analysis
  • Clustering
  • Measures of distance
  • Scaling and ordination
Text Books: Text books are not required for this paper.
Textbooks
Text books are not required for this paper.
Graduate Attributes Emphasised
Scholarship, Critical thinking.
View more information about Otago's graduate attributes.
Learning Outcomes
Demonstrate in-depth knowledge of the central concepts.

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Timetable

Second Semester

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

Lecture

Stream Days Times Weeks
Attend
L1 Wednesday 11:00-12:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Monday 11:00-12:50 29-34, 36-41
T2 Tuesday 15:00-16:50 29-34, 36-41
T3 Thursday 15:00-16:50 29-34, 36-41
T4 Friday 11:00-12:50 29-34, 36-41
T5 Friday 14:00-15:50 29-34, 36-41

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 Second Semester
Domestic Tuition Fees (NZD) $1,282.09
International Tuition Fees (NZD) $5,357.07

^ 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
  • Models for count data
  • Design of experiments
  • Principal components analysis
  • Clustering
  • Measures of distance
  • Scaling and ordination
Textbooks
Text books are not required for this paper.
Graduate Attributes Emphasised
Scholarship, Critical thinking.
View more information about Otago's graduate attributes.
Learning Outcomes
Demonstrate in-depth knowledge of the central concepts.

^ Top of page

Timetable

Second Semester

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

Lecture

Stream Days Times Weeks
Attend
L1 Wednesday 11:00-12:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Monday 11:00-12:50 29-34, 36-41
T2 Tuesday 15:00-16:50 29-34, 36-41
T3 Thursday 15:00-16:50 29-34, 36-41
T4 Friday 11:00-12:50 29-34, 36-41
T5 Friday 14:00-15:50 29-34, 36-41