Mathematics and Data Science, BSc

Course summary

OverviewData Scientist is in the top-ten of emerging jobs according to the LinkedIn emerging jobs report. Graduates that can combine their mathematical skills and statistical modelling to make sense of big data are in high demand. Studying BSc Mathematics and Data Science with us will provide a platform for you to enter this important sector. Additionally, as a Mathematics graduate you can find employment in any number of different careers including IT, finance and teaching.Why study BSc Mathematics and Data Science at Middlesex University?We believe strongly that the work you do must be relevant to the world of work – that's why our course has a strong practical slant. The core focus of your degree will be understanding the mathematical theory underpinning data science and learning within a practical setting to develop key skills for future employment.This degree offers an opportunity to obtain practical real-life experience of working and analysing big data, in addition to being taught and supported by staff that work and research in all areas of mathematics, with expert knowledge from the industry.You’ll build on theory to deliver practical solutions to a variety of real-world big data problems. The project-based assessment will give you a practical education and prepare you to apply your mathematical skills to one of the top emerging job sectors.Throughout your degree there will be scope to develop your programming and software skills, as well as learn new skills within a work environment through placement opportunities.Course highlightsModules that teach techniques from machine learning and artificial intelligenceProject and coursework based assessment, no end-of-year examsLarge, 60 credit third year project allows you to demonstrate the accumulation of your knowledge to develop a significant piece of work.

Year 1 Calculus and Geometry (30 credits) - Compulsory Mathematical Thinking (15 credits) - Compulsory Introduction to Programming (15 credits) - Compulsory Probability and Data Analysis (30 credits) - Compulsory Mathematical Models (15 credits) - Compulsory Linear Algebra (15 credits) - Compulsory Year 2 Problem Solving and Communication (30 credits) - Compulsory Software Design (15 credits) - Compulsory Discrete Mathematics (15 credits) - Compulsory Mathematics of Machine Learning (15 credits) - Compulsory Mathematical Statistics (30 credits) - Compulsory Advanced Calculus (15 credits) - Compulsory Year 3 Neural Networks and Deep Learning (30 credits) - Compulsory Mathematical Techniques for Optimisation (15 credits) - Optional Data Mining (15 credits) - Optional Time Series (15 credits) - Optional Cryptography and Blockchain (15 credits) - Optional Stochastic Processes for Finance (15 credits) - Optional Graph Theory (15 credits) - Optional Project (30 credits) - Compulsory