Mathematics and Statistics for Data Science

This module aims to cover the key statistical concepts and techniques you will need to interpret the results you might generate through data analysis.

The areas covered in this module include probability theory, likelihood, common distributions, confidence intervals, hypothesis tests, parametric and non-parametric tests.

Upon successful completion of this module, you will be able to:

  • demonstrate the ability to critically appraise and evaluate mathematical and statistical techniques for the given empirical/data analysis.
  • understand the physical significance of the given mathematical and statistical technique.
  • use the optimisation techniques in decision making.
  • use the statistically significant conclusions from the sample data.

Topics covered

  • Review of differential calculus
  • Vectors and matrices
  • Geometry of matrices and derivatives: linear transformations and partial derivatives
  • Descriptive Statistics: Data and Data Presentation, Measures of Location and Variability
  • Probability Theory
  • Probability Distributions
  • Sampling Distributions
  • Statistical Significance and Tests of Hypothesis
  • Analysis of Variance
  • Linear Regression and Correlation


15 (150 hours)


  • Summative coursework (30%)
  • Written examination (70%)