Subject code: MA5801:03
Data Science is grounded in mathematics. This subject will provide students with the essential elements of mathematics required for data scientists. This subject will include elements of discrete mathematics including logics, sets, proof, functions, relations, graphs and trees. It will also include elements of linear algebra including linear systems and matrix formulation, vector spaces, eigenvalues/eigenvectors, singular value decomposition as well as optimization and numerical methods. Computational aspects of this course will be developed in Matlab.
Professor White: Hi, my name is Ron White, I'm the Head of the School of Mathematics, Physics and Chemistry here at James Cook University.
The way we've set this course up is that we'll use our fundamental linear algebra and discrete math to seed some of the fundamental understandings of what we're going to do later on the course; some high-level statistics, computing, algorithm development, etc.
Associate Professor Belwad: My name is Shawn Ballard. I'm a mathematician - that's my training. I think this subject is really important for the data science program because it gives you the underpinnings of all the mathematics that gets embedded within all the algorithms that are going to be taught in your statistics subjects.
And you need to operationalise in a context using programming skills and so on. So we're giving you very much the underlying foundation and if you understand that foundation and you're much better off in terms of understanding the limitations and what you have at your disposal in terms of
- Identify and apply concepts of set theory, arithmetic, logic, proof techniques, binary relations, graphs and trees to solve problems in data science
- Apply linear algebra and numerical mathematics concepts for optimisation and dimensionality reduction in data science problems
- Apply and implement concepts in discrete mathematics, optimisation and linear algebra in data science using Matlab.
Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports.Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period.
This is one of the interdisciplinary subjects studied in the online Master of Data Science.
Please note, course structure and content are subject to change. For information on all course subjects download the course guide.
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