STATS 369 : Data Science Practice


2020 Semester Two (1205) (15 POINTS)

Course Prescription

Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.

Course Overview

This is a course on predictive modelling using real data.   and STATS 369 is required for the major in Data Science, but is also taken by undergraduate students from other majors in Science, Engineering, and Commerce who are interested in careers involving large-scale data analysis and modelling.  This course is good preparation for anyone wanting to do postgraduate study in Data Science. We emphasise understanding the modelling techniques in addition to being able to apply them using R. The predictive techniques covered include linear regression and discrimination, tree-based models, and neural networks. The course also covers the data cleaning and manipulation needed to prepare real-world data for analysis and some of the ethical issues that arise from the use of automated predictive models. The skills developed in this course are particularly useful for those wishing to have a career involving data science  and predictive modelling, which are areas in high demand.

Course Requirements

Prerequisite: STATS 220, 201 or 208, 210 or 225 Restriction: STATS 765

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Describe the 'tidy data' abstraction and its importance to data management and analysis (Capability 1 and 4)
  2. Explain over-fitting and cross-validation and why they are important in flexible predictive modelling (Capability 1 and 4)
  3. Fit predictive linear regression models to real data sets and evaluate their accuracy (Capability 1 and 3)
  4. Explain the concepts of ensembles and regularisation and their important in predictive modelling (Capability 1 and 4)
  5. Fit tree-based models to real data sets and evaluate their accuracy (Capability 1 and 3)
  6. Discuss the individual and social impacts of widespread use of accurate and inaccurate predictive models, and the ethical implications for data scientists (Capability 1, 2, 4 and 5)
  7. Fit neural network models to real data sets and evaluate their accuracy (Capability 1 and 3)
  8. Prepare data in the form needed for modelling when given a data set and relevant domain information (Capability 1 and 3)
  9. Choose an appropriate modelling technique and feature set and explain the choice when given a data set and relevant domain information (Capability 1, 3 and 4)


Assessment Type Percentage Classification
Final Exam 50% Individual Examination
Test 20% Individual Test
Assignments 20% Individual Coursework
Laboratories 10% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8 9
Final Exam

Learning Resources

There are two textbooks, both available from their authors' websites at no cost:

Grolemund & Wickham “R for Data Science” O’Reilly
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani "Introduction to Statistical Learning". Springer.

Special Requirements


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 1 hour lab, 2 hours of reading and thinking about the content and 4 hours of work on assignments and/or test preparation.

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.


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.

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.

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 at

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.

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.

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 08/07/2020 12:42 p.m.