Overview
This course is stackable to the Master of Science in Technology and Design (Data Science)
This introductory course offers a comprehensive foundation in both the theory and practical applications of statistical machine learning. As data-driven decision-making becomes increasingly central across sectors, the strategic use of statistical machine learning techniques is transforming how we do business, enhance quality of life, improve team performance in sports and connect with one another.
Through real-world examples, the course demonstrates how machine learning methods are applied in practice, while also unpacking the fundamental principles that underpin these techniques. Key topics include empirical risk minimisation, linear and logistic regression, nonparametric inference, model selection in high-dimensional settings, classification and regression trees, random forests and ensemble learning.
The course also covers emerging areas such as active learning, differential privacy, and ethical considerations in data analytics. Participants will gain hands-on experience implementing algorithms using the statistical software R, bridging theoretical insights with practical skills.
Course details
Course dates:
- 15 September to 20 December 2025
- Two sessions per week over a 14-week period, with a break during Week 7 (recess week).
Registration closing:
- 15 August 2025
Duration:
- 4 hours per week, delivered over 2 sessions weekly. Exact dates and times will be made available in August 2025.
You will attend classes alongside full-time Master of Science in Technology and Design students.
Who should attend
Participants who wish to upgrade your skillset in design and data science technology to fast-track your professional careers or entrepreneurial pursuits. Potential career paths include:
- Data Scientist/Analyst
- Operations Analyst/Manager
- Business Intelligence Analyst
- Financial Analyst
- Supply Chain Analyst
- Healthcare Data Analyst
- Consultant in Data Science and Analytics
Prerequisites
- Students should have a basic understanding of linear algebra, calculus, and basic probability and statistics.