Key facts
UNE unit code: STAT430
*You are viewing the 2027 version of this unit which may be subject to change in future.
- Trimester 1 - On Campus
- Trimester 1 - Online
- Armidale Campus
- Yes
- No
- Yes
- 6
Unit information
Data analysis has been transformed in recent years through the huge increase in data collection and advances in computational methods. This has led to rapidly evolving methods in statistical learning and is one of the core research areas in statistics and computer science.
With a focus on applications, this unit introduces you to modern approaches to computational data analysis, whether you are interested in further study or research in an area of science, or you want to learn about applications for marketing, finance and other business disciplines.
Exploring cutting-edge topics, you will discuss regression models, linear discriminant analysis, model selection and regularisation (choosing the optimal model, dimension reduction methods, ridge and lasso), tree-based methods such as random forest and boosting, resampling methods and support-vector machines. You will also cover some unsupervised learning methods, including principal components and clustering.
Offerings
For further information about UNE's teaching periods, please go to Principal Dates.
| Teaching period | Mode/location |
|---|---|
| Trimester 1 | On Campus, Armidale Campus |
| Trimester 1 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
Students will be required to install R, a freely available (open source) downloadable cross-platform application.
Please refer to the student handbook for current details on this unit.
Unit coordinator(s)
Learning outcomes
Upon completion of this unit, students will be able to:
- implement various statistical learning techniques in R to analyse complex datasets, interpret and communicate results and conclusions in a broad range of contexts;
- apply critical thinking and understanding of the rationale behind the formulation and components of common statistical models;
- enhance and broaden their knowledge of the theoretical and computational underpinnings of various statistical procedures; and
- demonstrate a high level of understanding and highly developed communication skills by independently and critically analysing a special topic in advanced computational model.
Assessment information
Assessments are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
| Title | Must Complete | Weight | Offerings | Assessment Notes |
|---|---|---|---|---|
| Assessment 1 | Yes | 10% | All offerings | Problem solving / Data analysis assignment. |
| Assessment 2 | Yes | 20% | All offerings | Problem solving / Data analysis assignment. |
| Assessment 3 | Yes | 20% | All offerings | Problem solving / Data analysis assignment. |
| Oral Examination - Assurance Task | Yes | 30% | All offerings | Students prepare and submit working for a data analysis task, then defend their approach and their knowledge of unit content in an oral examination. Students must pass Oral and Practical Exams in aggregate to pass the unit. |
| Practical Examination - Assurance Task | Yes | 20% | All offerings | Students undertake a data analysis task under proctored conditions. Students must pass Oral and Practical Exams in aggregate to pass the unit. |
Learning resources
Textbooks are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
Note: Students are expected to purchase prescribed material. Please note that textbook requirements may vary from one teaching period to the next.
An Introduction to Statistical: Learning with Applications in R
ISBN: 9781707161417
James, G., Witten, D., Hastie, T. and Tibshirani, R., Springer 2nd Edition 2021
Text refers to: All offerings
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