BSc (Hons) Data Science Bachelor's degree at the University of Leeds

You'll learn the foundational concepts of function, number and proof, equipping you with the language and skills to tackle your mathematical studies. The module also consolidates basic calculus, extending it to more advanced topics, such as functions of several variables. This leads to methods for solving simple ordinary differential equations. Linear algebra provides a basis for wide areas of mathematics and this module provides the essential foundation.

In the modern scientific and technological world, it is even more important to understand probabilistic and statistical arguments. This module will introduce you to key ideas in both areas, with probability forming the theoretical basis for statistical tests and inference.

You'll be introduced to computational techniques, algorithms and numerical solutions, as well as the mathematics of discrete systems. You'll learn basic programming using the language Python and apply computational techniques to the solution of mathematical problems.

This module examines the nature of data science, the data science lifecycle and the strategic organisational role of a data scientist. You'll be introduced to a high-level approach to analysis of data in different formats and this module also lays the foundation to work at the level of raw data, experimentation and reproducible programming pipelines. A key aspect is the interdisciplinary and integrated nature of the subject, in particular the social science dimension around communication, collab

This module lays the foundations for computer programming for big data, with basics of procedural and functional programming, data structures and good programming practice such as reproducibility, collaborative editing and version control. You'll encounter and practise techniques for handling big data such as parallelisation, vectorisation and high-performance computing, and for the design of efficient algorithms. These are applied to exploratory data analysis, computational modelling, problem s

Explore the more abstract ideas of vector spaces and linear transformations, together with introducing the area of discrete mathematics (such as graphs and networks), both of which are of fundamental importance to data science

You'll be introduced to technical real-world considerations such as the analysis of networks and complex systems. The module builds on the mathematical foundations of algebra and graph theory and applies these to the analysis of networks. It compares and contrasts reductionism & superposition with non-linearity & complexity such as the messiness of the real world. Building on Computational Mathematics and Modelling, the module combines ideas of networks with non-linear activation functions in or

Develop your programming skills for applications to software engineering and machine learning. You'll encounter the object-oriented programming paradigm, as well as software engineering practices around design thinking, resource trade-offs and project management tools. Fundamental machine learning paradigms such as supervised, unsupervised and reinforcement learning are introduced and key topics in data science such as clustering, genetic algorithms, random forests and dimensionality reduction a

Statistical models are important in many applications. They contain two main elements: a set of parameters with information of scientific interest and an "error distribution" representing random variation. This module lays the foundations for the analysis of such models. We'll use practical examples from a variety of statistical applications to illustrate the ideas.

A stochastic process refers to any quantity which changes randomly in time. The capacity of a reservoir, an individual's level of no claims discount and the size of a population are all examples from the real world. The linking model for all these examples is the Markov process. With appropriate modifications, the Markov process can be extended to model stochastic processes which change over continuous time, not just at regularly spaced time points. You'll explore the key features of stochastic

In time series, measurements are made at a succession of times, and it is the dependence between measurements taken at different times which is important. This module will concentrate on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.

This module builds on previous modules that cover Machine Learning and Neural Networks, and develops more advanced topics in areas like Artificial Intelligence and data mining, applied to key sectors such as finance, health, energy, sustainability etc. Key topics are Deep Learning and Explainable AI, for instance in the context of natural language processing, unstructured data labelling, or object recognition, as well as the ethics of responsible AI. It reflects on the data science lifecycle in