STATS 765 : Statistical Learning for Data Science


2024 Semester One (1243) (15 POINTS)

Course Prescription

Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.

Course Overview

This is a course on predictive modelling using real data. It is intended for students taking the 240-point Master of Data Science. The predictive techniques covered include linear regression and discrimination, tree-based models, and neural networks. The course also covers the 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 course emphasises understanding the modelling techniques in addition to being able to apply them using R. 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: 15 points from ENGSCI 314, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707 Corequisite: May be taken with STATS 707 Restriction: STATS 369

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 8: Ethics and Professionalism
Graduate Profile: Master of Data 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 3, 4 and 5)
  2. Explain over-fitting and cross-validation and why they are important in flexible predictive modelling (Capability 3, 4, 5 and 6)
  3. Fit predictive linear regression models to real data sets and evaluate their accuracy (Capability 3, 4 and 5)
  4. Explain the concepts of ensembles and regularisation and their important in predictive modelling (Capability 3, 4, 5 and 6)
  5. Fit tree-based models to real data sets and evaluate their accuracy (Capability 3, 4 and 5)
  6. Discuss the individual and social impacts of widespread use of accurate and inaccurate predictive models, and the ethical implications for data scientists (Capability 3, 4, 5, 6 and 8)
  7. Fit neural network models to real data sets and evaluate their accuracy (Capability 3, 4 and 5)
  8. Prepare data in the form needed for modelling when given a data set and relevant domain information (Capability 3, 4 and 5)
  9. Choose an appropriate modelling technique and feature set and explain the choice when given a data set and relevant domain information (Capability 3, 4 and 5)


Assessment Type Percentage Classification
Final Exam 50% Individual Examination
Test 20% Individual Test
Laboratories 10% Individual Coursework
Project 20% Group & Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8 9
Final Exam
This course does not have plussage, nor a minimum exam mark.  The minimum pass mark required is an overall of 50% from the combined assessments (exam, test, labs, project).


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

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, a typical weekly workload includes:

  • 3 hours of lectures
  • A 1-hour tutorial
  • 2 hours of reviewing the course content
  • 6 hours of work on assignments and/or test preparation

Delivery Mode

Campus Experience

Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the test and exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning 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.

  • R for Data Science, by Grolemund and Wickham, available on their website
  • Introduction to Statistical Learning (2nd edition), by James, Witten, Hastie and Tibshirani, available on their website

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.

Based off students’ feedback and comments, we would provide support and clear guidance for the assessments. For instance, clarifying project task expectation, goals, and the grading schema ahead of each milestone.

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 for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

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 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.

Published on 06/11/2023 08:40 a.m.