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|
|Teaching period||First Semester|
|Domestic Tuition Fees (NZD)||$1,142.40|
|International Tuition Fees (NZD)||$4,661.93|
- 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.
- 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.
- More information link
- View more information for STAT 442
- 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
- 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 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.