Data Science - BSc (Hons)

Developed with input from industry experts, this course covers all the necessary skills and competencies required to delve deeper into this fascinating field. By the end of the BSc degree, you’ll be ready to apply for rewarding roles in the data science and big data industries, as well as the many sectors and organisations that increasingly require data scientists.

Course details

This Data Science BSc course offers a comprehensive introduction to the most important areas of the discipline, including data programming, statistical modelling, business intelligence, machine learning and data visualisation.

  • Mode of study: 2 -3 days campus
  • Intake: September , January
  • Course length: 3-4 years
  • Course fee: £9,250 per year
  • Location: London

London Metropolitan University

Course overview

Designed by academics from both Mathematics and Applied Computing backgrounds, this course is made up of fine-tuned modules which are prepared with your future in mind. The course will foster your learning development using a range of tools and big data platforms, allowing you to continue to specialise in data engineering, analytics, big data visualisation, statistical modelling and machine learning.


Entry requirements

a minimum grade C in three A levels (or a minimum of 96 UCAS points from an equivalent Level 3 qualification, eg BTEC Level 3 Extended Diploma, Advanced Diploma, Progression Diploma or Access to Higher Education Diploma of 60 Credits)

English language and Mathematics GCSEs at grade C/4 or above (or equivalent)

What the students say

The highlight of my time at London Met so far has been using the resources made available by the University, especially the careers department, along with the skills obtained on my degree to successfully secure an intern finance position in my first year.


The modules listed below are for the academic year 2022/23 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

  • Data Analysis and Financial Mathematics (core, 30 credits)
  • Fundamentals of Computing (core, 15 credits)
  • Introduction to Information Systems (core, 15 credits)
  • Logic and Mathematical Techniques (core, 30 credits)
  • Programming (core, 30 credits)
  • Data Analytics (core, 15 credits)
  • Data Engineering (core, 15 credits)
  • Databases (core, 15 credits)
  • Professional Issues, Ethics and Computer Law (core, 15 credits)
  • Programming with Data (core, 15 credits)
  • Smart Data Discovery (core, 15 credits)
  • Statistical Methods and Modelling Markets (core, 30 credits)
  • Artificial Intelligence and Machine Learning (core, 15 credits)
  • Big Data and Visualisation (core, 15 credits)
  • Project (core, 30 credits)
  • Academic Independent Study (option, 15 credits)
  • Advanced Database Systems Development (option, 30 credits)
  • Artificial Intelligence (option, 15 credits)
  • Cryptography and Number Theory (option, 15 credits)
  • Ethical Hacking (option, 15 credits)
  • Financial Modelling and Forecasting (option, 30 credits)
  • Formal Specification & Software Implementation (option, 30 credits)
  • Work Related Learning II (option, 15 credits)