STATS 784 : Statistical Data Mining


2021 Semester One (1213) (15 POINTS)

Course Prescription

Data cleaning, missing values, data warehouses, security, fraud detection, meta-analysis, and statistical techniques for data mining such as regression and decision trees, modern and semiparametric regression, neural networks, statistical approaches to the classification problem.

Course Overview

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).

Course Requirements

Prerequisite: 15 points from STATS 210, 225, and 15 points from STATS 330, 762

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Master fundamental material such as the binary prefixes, big-Oh, and appreciate the role of data science in society. (Capability 1 and 4)
  2. 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)
  3. Competently be able to fit 2 or 3 methods well, such as decision trees and generalized additive models. (Capability 2, 3 and 4)
  4. Use R efficiently to solve BigData problems, including graphics. (Capability 1, 3 and 4)


Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Test 30% Individual Test
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4
Final Exam


Not applicable.

Key Topics

  • 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.

Special Requirements

There is no plussage. Students should complete the assignments, test and exams as best they can. Attendance of lectures are expected. Lecture recordings will be placed on Canvas.

Workload Expectations

 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.

Delivery Mode

Campus Experience

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.

Learning Resources

A coursebook will be provided on Canvas chapter by chapter as the course progresses... only this material will be examinable. It will be in .pdf format. Suggested reading lists will be given at the end of each chapter but this is very optional. A study guide at lecture 1 will list some optional overall background reading. Past exams and tests will be available but note that the course has evolved over time and the current material covered has changed significantly.

Student Feedback

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.

Digital Resources

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.

Computers in the lab will be sufficient, however students are welcome to use their own devices.
Students may hand-write their assignments, however using Rmarkdown or equivalent, is probably better.
The lecturer will set up a class webpage to put supplementary material that might be of interest to the class (FYI only).

Academic Integrity

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.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

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.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Here is some general information on how 784 will run under different Covid-19 Alert Levels.

Level 1: Delivered normally as specified in delivery mode.

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


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.

Published on 04/03/2021 01:53 p.m.