This paper provides an overview of ideas and methods that are useful when analysing big data.
About this paper
| Paper title | Topic in Advanced Statistics |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Not offered in 2026 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,393.95 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- 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
- 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.
This paper provides an overview of ideas and methods that are useful when analysing big data.
About this paper
| Paper title | Topic in Advanced Statistics |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Not offered in 2027 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- 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.
- Contact
- Teaching staff
The paper will be taught by academic staff from the University of Canterbury and the University of Otago. The paper 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.