Overview
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.
About this paper
| Paper title | Statistical Techniques for Data Science |
|---|---|
| Subject | Information Science |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period(s) | Semester 2
(On campus)
Semester 2 (Distance learning) |
| Domestic Tuition Fees ( NZD ) | $1,409.28 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- 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
- Paper Structure
Main topics:
- Linear and logistic regression
- Multivariate analysis
- Design of experiments
- Principal components analysis
- Penalised methods
- Classification
- Clustering
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Scholarship, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
- Demonstrate in-depth knowledge of the central concepts.
Timetable
Overview
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.
About this paper
| Paper title | Statistical Techniques for Data Science |
|---|---|
| Subject | Information Science |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period(s) | Semester 2
(Distance learning)
Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2024 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- 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
- Paper Structure
Main topics:
- Linear and logistic regression
- Multivariate analysis
- Design of experiments
- Principal components analysis
- Penalised methods
- Classification
- Clustering
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Scholarship, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
- Demonstrate in-depth knowledge of the central concepts.