STATS 784 : Statistical Data Mining
2021 Semester One (1213) (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 may try let you work with at least one `large' data set, and there may be some R programming. Students are required to have a good background in statistics---both theoretically and computationally (R).
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. (Capability 2, 3 and 4)
- Use R efficiently to solve BigData problems, including graphics. (Capability 1, 3 and 4)
|Final Exam||40%||Individual Examination|
|Assessment Type||Learning Outcome Addressed|
- What is data mining?
- Handling large data sets in R and Linux
- Data visualization
- Decision trees
- VGLMs and VGAMs (especially for estimation and prediction)
- The classification problem (time allowing)
- Compared to previously, I hope to cover the following new topics: 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. Attendance on campus is required for the test (if under Alert Level 1) which is scheduled to be during a class time. The activities for the course are scheduled as a standard weekly timetable.
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
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If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
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Level 2: You will not be required to attend in person. All teaching and assessment will have a remote option. The following
activities will also have an on campus / in person option: Lectures, office hours.
Level 3 / 4: All teaching activities and assessments are delivered remotely.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.