University of London logo

International Foundation Programme for Computer Science

Applications are open!

University of London logo

International Foundation Programme for Computer Science

University of London

Applications are open!

Applications for the July 2025 cohort are now open. Apply now or express interest to get started.

Apply now

International Foundation Programme for Computer Science

Offered by the University of London

Get degree-ready

On successful completion of this programme, students will be offered a place on the UoL online BSc Computer Science degree on Coursera

£4,500 total cost

With flexible pay by module tuition, you’ll only pay for the current module you’re studying.

6 - 36 months

Complete the course in as little as six months - or study at a pace that suits you, with up to three years to complete the programme.

100% online

Lecture videos, live sessions, group feedback sessions, and connection with instructors and peers.

Academics

Get degree-ready, with foundational skills in programming, mathematics and statistics

The International Foundation Programme (IFP) for Computer Science gives you the knowledge and skills you need to progress to the University of London’s online BSc Computer Science degree.

You’ll gain valuable hands-on programming experience, using Python to gain understanding of loops, variables, data types and functions. Using Tableau, you’ll learn to tell stories with data, creating and customising dashboards to communicate with a range of audiences. You’ll also learn to apply statistical analysis tools like causal models and inference methods to solve real-world problems.

With extensive experience in distance learning since 1858, you’ll enjoy engaging, hands-on competency-based learning modules created using UoL’s unique CAFE (content, activity, feedback, evaluation) methodology.

To give you the best possible preparation for your subsequent degree programme, the IFP for Computer Science also provides a computing-specific academic skill building component. This module will increase your data literacy and ensure your ability to create properly referenced critiques and arguments, drawing on a range of academic texts and other research sources.

Through the University of London’s comprehensive approach, you’ll also be able to fill any gaps in your mathematics knowledge so that you can successfully progress onto the online BSc Computer Science degree.

Flexibility
Work through the programme on your own time - with up to three years to complete the programme, you’ll have the flexibility to choose and vary your course load.
Programme length
Complete this course in as little as six months, and fast-track your progression to the University of London’s online BSc Computer Science.
Live sessions and virtual office hours
Ask questions and get answers, with feedback and learning reinforcement from your online tutor and peers through Zoom, group sessions and discussion forums.
Learn on mobile
Take your studies on the go with mobile-friendly learning on iOS and Android (some assignments can’t be completed on a mobile device).

Applications for July 2025 are now open!

Interested in learning more? Catch up on our latest webinar with University of London faculty. Watch the recording now.

To join this programme, you must be age 18+ by 31 December in the year of registration.

⏰ Important Dates for July 2025:

  • Application deadline June 2
  • Registration deadline June 16
  • Classes start June 30

Applications for July 2025 are now open!

Interested in learning more? Catch up on our latest webinar with University of London faculty. Watch the recording now.

To join this programme, you must be age 18+ by 31 December in the year of registration.

⏰ Important Dates for July 2025:

  • Application deadline June 2
  • Registration deadline June 16
  • Classes start June 30

Build the skills you’ll need in programming, statistics and data analysis

Build a solid foundation for computer science expertise, with key programming concepts and problem-solving techniques using Python, one of the most widely used and versatile programming languages.

A focus on real-life problems and practical exercises will provide you with a strong working knowledge and sought-after computer science skills.

Curriculum

You’ll build computing-specific academic skills across two distinct areas:

  • Qualitative reasoning skills: Exploring computer ethics, you’ll learn to think critically and communicate effectively about legal, moral and ethical issues related to your field of study.

  • Quantitative reasoning skills: You’ll use data analysis and produce engaging dashboards, apply common descriptive statistics to summarise datasets, and use data visualisation approaches to demonstrate patterns.

  • This module will provide practical experience in applying theoretical programming concepts in creative ways to solve real-world problems. Using Python, one of the most popular and versatile programming languages, you’ll build essential skills in this vital discipline.
  • On completion of this module, you’ll be proficient in implementing and manipulating data structures, managing and analysing data.
  • Topics covered include control flow statements, data structures, object-oriented programming, data wrangling with Pandas, databases and NumPy library.

  • This module provides a comprehensive knowledge base in the mathematics that underpin computer science.
  • You’ll refresh and secure your understanding of arithmetic and algebra, gain an overview of the functions and fundamentals of calculus and trigonometry, and explore a range of other mathematical concepts that will be relevant to your ongoing studies of computer science.
  • Topics covered include quadratic equations and parabolae, number bases and modular arithmetic, sequences and series, and vectors and geometry.

  • You’ll build a strong foundation of statistical knowledge on this module. You’ll grasp the fundamental principles of probability theory, allowing you to understand and apply standard statistical operators and recall essential probability distributions.
  • You’ll perform statistical inference in order to make informed decisions based on data analysis, and construct causal models to meet the requirements of different statistical analysis contexts.
  • Topics covered include discrete and continuous probability distributions, interval estimation, hypothesis testing principles, contingency tables and the chi-squared test, and simple linear regression.

Applications are open now!

Apply now to secure your spot