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STAT442 Topic in Advanced Statistics

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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 Semester 1 (On campus)
Domestic Tuition Fees (NZD) $1,154.90
International Tuition Fees (NZD) $4,801.79

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Notes
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.
Eligibility
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.
Contact

mparry@maths.otago.ac.nz

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

Topics:

  • 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

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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Timetable

Semester 1

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

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 2022 (On campus)
Domestic Tuition Fees Tuition Fees for 2022 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Notes
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.
Eligibility

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.

Contact

martin.hazelton@otago.ac.nz    

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

Topics:

  • 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

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.

^ Top of page

Timetable

Not offered in 2022

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