Course overview
The aim of this course is to provide students with working knowledge of mathematical and statistical concepts relevant to applications in data analytics. The aim of this course is topics covered in this course include: linear algebra concepts, vectors and matrices operations, eigenvalues and eigenvectors, dynamical systems, optimisation techniques (e.g. Gradient descent), linear regression, probability concepts (probability laws, Bayes' rule and independence), selected probability distributions, statistical inference and applications to data analytics.
Course learning outcomes
- Demonstrate working knowledge of linear algebra, including vectors and matrices to solving problems.
- Apply selected optimisation techniques in the context of data analytics.
- Apply probability concepts and statistical methods to solving data analytical problems.
- Communicate results of mathematical modelling in context of data analytics.
Degree list
The following degrees include this course