Program Overview

Join our BSc Mathematics and Statistics with Data Science with a Placement Year degree and develop the skills you'll need to become a quantitative scientist.

This four-year course includes modules in all three subject areas – mathematics, statistics, and data science – so you will be able to work confidently in the three disciplines and bridge the gaps between them.

Vast amounts of data are being collected about almost every aspect of the world. Data science and machine learning are now part of our everyday lives; they are applied to process and interpret this data, perform in-depth analyses with the aim of extracting valuable information, and build artificial intelligence (AI) models.

Expertise in data science, statistics and mathematics are thus becoming increasingly important in a wide range of areas including finance and business, climate science, medicine and the civil service.

This course, taught by University of Reading’s Department of Mathematics and Statistics, has been designed to develop your expertise in mathematics and statistics theory. You’ll also build your knowledge of applying statistics and data science methods to real-world problems and increasingly complex data.
97% of students in the Department of Mathematics and Statistics said our teaching staff were good or very good at explaining things (National Student Survey, 2023).

This degree is accredited by the Institute of Mathematics and its Aapplications to meet the educational requirements of the Chartered Mathematician designation when followed by subsequent training and experience in employment to obtain competencies to those specified by the Quality Assurance Agency (QAA) for taught master's degrees.

What you’ll study with BSc Mathematics and Statistics with Data Science with a Placement Year

In mathematics you will study:

• calculus
• analysis
• linear algebra
• differential equations.

In statistics and data science you will cover:

• linear models
• statistical computing
• probability
• regression and classification methods
• unsupervised machine learning.