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. 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) |
| Delivery mode | The Distance Learning offering of this paper is taught and assessed remotely |
| Domestic Tuition Fees ( NZD ) | $1,627.83 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 110 or STAT 115
- 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
- Teaching staff
- Paper Structure
Main topics:
- Introduction to statistics
- Linear regression
- Analysis of variance
- Interaction
- Model building
- Logistic regression
- Time series analysis
- Simulation
- Sampling
- Principal component analysis
- Clustering
- Classification
- Smoothing
- Generalised additive models
- Penalised regression
- 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
a) Apply important statistical techniques to real data;
b) Describe the assumptions underlying use of each of these methods;
c) Understand key statistical ideas related to the use of probabilistic models, model selection and quantification of uncertainty;
d) Critically appraise literature in terms of the statistical methods used;
e) Use a standard statistical programming language (R) to analyse data.
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. 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) |
| Delivery mode | The Distance Learning offering of this paper is taught and assessed remotely |
| 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. |
- Prerequisite
- STAT 110 or STAT 115
- 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
- Teaching staff
- Paper Structure
Main topics:
- Introduction to statistics
- Linear regression
- Analysis of variance
- Interaction
- Model building
- Logistic regression
- Time series analysis
- Simulation
- Sampling
- Principal component analysis
- Clustering
- Classification
- Smoothing
- Generalised additive models
- Penalised regression
- 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
a) Apply important statistical techniques to real data;
b) Describe the assumptions underlying the use of each of these methods;
c) Understand key statistical ideas related to the use of probabilistic models, model selection and quantification of uncertainty;
d) Critically appraise literature in terms of the statistical methods used;
e) Use a standard statistical programming language (R) to analyse data.