Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

STAT442 Topic in Advanced Statistics

Details available from the Department of Mathematics and Statistics.

This is a new paper that provides an overview of the ideas and methods that are useful when analysing the massive datasets that are becomingly increasingly common.

Paper title Topic in Advanced Statistics
Paper code STAT442
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,076.55
International Tuition Fees (NZD) $4,267.52

^ Top of page

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 or twang@maths.otago.ac.nz
Teaching staff
The paper will be taught by academic staff from several universities.

The course will be delivered by lectures using videoconferencing techology between a number of NZ universities.

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
  • Selection bias and multiple testing
Teaching Arrangements
Twelve 2-hour lectures
Textbooks
Textbooks are not required for this paper.
Course outline
View course outline for STAT 442
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 manage and analyse a very large dataset and to communicate the information obtained from the analysis.

^ Top of page

Timetable

First Semester

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 First Semester
Domestic Tuition Fees Tuition Fees for 2018 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 (STAT 110, STAT 115 or BSNS 102) and two further papers at 200/300-level that include experience in quantitative research methods or applied statistics before enrolling in STAT 442. 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 or twang@maths.otago.ac.nz
Teaching staff
The paper will be taught by academic staff from several universities.

The course will be delivered by lectures using videoconferencing techology between a number of NZ universities.

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.
Course outline
View course outline for STAT 442
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

First Semester

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