STATS 784 : Statistical Data Mining
2022 Semester One (1223) (15 POINTS)
This course was the first in the department on data mining and was (and still is) intended to be both practical and theoretical. Anybody wanting to use R for regression or classification on big data sets should benefit, as well as research students. So we will look at some statistical theory and practical aspects of data mining. This provides an opportunity to encounter some trendy methods such as random forests. It will have a significant coursework component, most of it being computer work. I might try let you work with at least one `large' data set. Students need to do some basic R programming. Students are required to have a good background in statistics---both theoretically and computationally (R). The early quiz will help assess your background. Note that the following topics from the Course Prescription will not be covered: data warehouses and meta-analysis.
Capabilities Developed in this Course
|Capability 1:||Disciplinary Knowledge and Practice|
|Capability 2:||Critical Thinking|
|Capability 3:||Solution Seeking|
|Capability 4:||Communication and Engagement|
- Master fundamental material such as the binary prefixes, big-Oh, and appreciate the role of data science in society. (Capability 1 and 4)
- Critically evaluate and explain fundamental statistical concepts such as under- and over-fitting, parametric and nonparametric methods, and the curse of dimensionality, within the context of BigData. (Capability 2, 3 and 4)
- Competently be able to fit 2 or 3 methods well, such as decision trees and generalized additive models, including smoothing as a very useful tool. (Capability 2, 3 and 4)
- Use R efficiently to solve BigData problems, including traditional regression using S formulas, and graphics. (Capability 1, 3 and 4)
|Final Exam||40%||Individual Examination|
|Assessment Type||Learning Outcome Addressed|
For more information and to find contact details for the Department Tuākana coordinator, please see https://www.auckland.ac.nz/en/science/study-with-us/maori-and-pacific-at-the-faculty/tuakana-programme.html
- What is data mining? A wide-ranging overview including unit -prefixes and big-Oh.
- Handling large data sets in R and Linux. The focus is on efficient computing and R programming.
- Data visualization for big data sets.
- Decision trees. Many important statistical concepts are illustrated using regression and classification trees.
- Vector generalized linear and additive models (VGLMs/VGAMs), especially for regression, estimation and prediction.
- Generally-altered, -inflated, -truncated and -deflated (GAITD) regression, especially for heaped and seeped data.
- The classification problem (time allowing).
- The following semi-new topics will be interleaved with the above: variable selection via the lasso, dimension-reduction, random forests, gradient boosting.
This course is a standard 15 point course and students are expected to spend 150 hours per semester involved in each 15 point course that they are enrolled in. For this course you can expect 3 hours of lectures, a 1-hour tutorial, 2 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation each week.
This course is available for those who are remote but the course is primarily aimed at those in Auckland. Lectures will be available as recordings and these will be placed on Canvas so overseas students will need fast internet. For those in Auckland, attendance is expected at lectures but no credit is given for this. Other learning activities such as tutorials/labs (if any) will not be available as recordings. The course will not include live online events including tutorials. If the vast majority of the class is in Auckland, then attendance on campus is required for the test for those in Auckland, and the test is scheduled to be during a class time. If many students are based overseas then the test will be wholly online, for consistency. The activities for the course are scheduled as a standard weekly timetable.
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
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The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.
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Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.