Major structure
Overview
In this major you’ll develop a strong foundation in the statistical aspects of data analysis (data collection, data mining, modelling and inference) and the principles of computer science (algorithms, data structures, data management and machine learning).
Your major structure
You’ll complete this major as part of a Bachelor of Science degree.
In your first and second years you’ll complete subjects that are prerequisites for your major, including mathematics, statistics and computing.
In your third year, you will complete 50 points (four subjects) of deep and specialised study in data science.
Throughout your degree you’ll also take science elective subjects and breadth (non-science) subjects.
Read more about studying mathematics and statistics at the University of Melbourne.
Sample course plan
View some sample course plans to help you select subjects that will meet the requirements for this major.
Showing sample course plan:
These sample study plans assume that students have achieved a study score of at least 29 in VCE Specialist Mathematics 3/4, or equivalent. If students have not completed this previously, they may first need to enrol in MAST10005 Calculus 1 in their first semester.
Year 1
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
Year 2
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
Year 3
100 pts
- Semester 1 50 pts
- Semester 2 50 pts
Explore this major
Explore the subjects you could choose as part of this major.
- Machine Learning12.5 pts
AIMS
Machine Learning, a core discipline in data science, is prevalent across Science, Technology, the Social Sciences, and Medicine; it drives many of the products we use daily such as banner ad selection, email spam filtering, and social media newsfeeds. Machine Learning is concerned with making accurate, computationally efficient, interpretable and robust inferences from data. Originally borne out of Artificial Intelligence, Machine Learning has historically been the first to explore more complex prediction models and to emphasise computation, while in the past two decades Machine Learning has grown closer to Statistics gaining firm theoretical footing.
This subject aims to introduce undergraduate students to the intellectual foundations of machine learning, and to introduce practical skills in data analysis that can be applied in graduates' professional careers.
CONTENT
Topics will be selected from: prediction approaches for classification/regression such as k-nearest neighbour, naïve Bayes, discriminative linear models, decision trees, Support Vector Machines, Neural Networks; clustering methods such as k-means, hierarchical clustering; probabilistic approaches; exposure to large-scale learning.
- Linear Statistical Models12.5 pts
Linear models are central to the theory and practice of modern statistics. They are used to model a response as a linear combination of explanatory variables and are the most widely used statistical models in practice. Starting with examples from a range of application areas this subject develops an elegant unified theory that includes the estimation of model parameters, quadratic forms, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Both full rank models and models that are not of full rank are considered. The theory is illustrated using common models and experimental designs.
- Modern Applied Statistics12.5 pts
Modern applied statistics combines the power of modern computing and theoretical statistics. This subject considers the computational techniques required for the practical implementation of statistical theory, and includes Bayes and Monte-Carlo methods. The subject focuses on the application of these techniques to generalised linear models, which are commonly used in the analysis of categorical data.
- Applied Data Science12.5 pts
AIMS
This capstone subject for the Data Science major combines statistical reasoning and practical computing skills to solve challenging problems with big data.
INDICATIVE CONTENT
Students will learn about communication of quantitative information and insights; presentation skills; report writing; project management; problem formulation using case studies; data collection and measurement protocols; data from surveys and experiments; issues in capturing and dealing with “big data”; dimension reduction; data visualisation; fitting formulated models to data to infer insightful information about populations; ethics in quantitative research; working effectively in teams.