Mathematics for Data Science
A strong grasp of mathematics is key to the correct interpretation of the results of data analysis.
This module aims to provide you with the key mathematical concepts and techniques you will need to interpret results generated through data analysis. The areas covered include probability theory, likelihood, common distributions, confidence intervals, hypothesis tests, parametric and non-parametric tests.
- Invertible matrices
- Systems of linear equations and Gaussian elimination
- Eigenvalues and eigenvectors
- Matrix diagonalization
- Partial differentiation and multivariate chain rule
- Maxima and minima and Lagrange multipliers
- Gradients of vector-valued functions and matrices
- Higher order derivatives & Linearisation and multivariate Taylor series
- Gradient descent and Multi-linear Regression
15 (150 hours)
- Coursework (30%)
- Written examination (70%)