Course overview
Data science is one of the highest-paying graduate jobs, for those with the relevant mathematical training. This course introduces fundamental mathematical concepts relevant to data and computer science and provides a basis for further study in data science, statistics and cybersecurity. Topics covered are probability: sets, counting, probability axioms, Bayes theorem; applications of calculus: integration and continuous probability distributions, series approximations; linear algebra: vectors and matrices, matrix algebra, vector spaces, eigenvalues and diagonalisation. The course draws connections between each of these fundamental mathematical concepts and modern data science applications and introduces mathematical applications using Python programming.
Course learning outcomes
- Demonstrate understanding of basic mathematical concepts in data science, relating to linear algebra, probability, and calculus.
- Employ methods related to these concepts in a variety of data science applications.
- Apply logical thinking to problem-solving in context.
- Use appropriate technology to aid problem-solving and data analysis.
- Demonstrate skills in writing mathematics.
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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.