Computer Science
OVERVIEW
The OCR A-level Computer Science course covers a broad range of topics, giving students the skills and knowledge to understand how technology works in the real world and how to solve complex problems using technology.
You will develop practical programming skills, understand algorithms, design computer systems, analyse data and develop computational thinking.
This course is ideal for students interested in technology, engineering, problem-solving and innovation.
You’ll learn to code, analyse data, and build exciting projects.
These future-proof skills are in high demand in both universities and by future employers. Unleash Your Imagination: turn ideas into reality. Whether you’re into AI, cybersecurity, or web design, computer science lets you flex your creative muscles. Imagine being part of the team that invents the next Instagram or launches a mission to Mars?
Computer Science opens doors to exciting careers in software development, data science, and beyond. It isn’t just about lines of code; it’s about shaping the future.
Modules
1.1 The characteristics of contemporary processors, input, output and storage devices – Components of a computer and their uses
1.2 Software and software development – Types of software and the different methodologies used to develop software
1.3 Exchanging data – How data is exchanged between different systems
1.4 Data types, data structures and algorithms – How data is represented and stored within different structures. Different algorithms that can be applied to these structures
1.5 Legal, moral, cultural and ethical issues – The individual moral, social, ethical and cultural opportunities and risks of digital technology. Legislation surrounding the use of computers and ethical issues that can or may in the future arise from the use of computers
2.1 Elements of computational thinking Understand what is meant by computational thinking
2.2 Problem solving and programming How computers can be used to solve problems and programs can be written to solve them
2.3 Algorithms The use of algorithms to describe problems and standard algorithms
Individual Programming Project
3.1. Analysis of the problem
3.2 Design of the solution
3.3 Developing the solution
3.4 Evaluation
Incredibly relevant and useful in a rapidly changing employment landscape, the course will require pupils to complete three assessments in the following areas:
Paper 1: Computer Systems.
- The characteristics of contemporary processors, input, output and storage devices
- Software and software development
- Exchanging data
- Data types, data structures and algorithms
- Legal, moral, cultural and ethical issues
Paper 2: Algorithms and Programming.
- Elements of computational thinking
- Problem solving and programming
- Algorithms to solve problems and standard algorithms
Non-Examination Assessment (NEA):
Produce their own programming project.
- The learner will choose a computing problem to work through according to the guidance in the specification.
- Analysis of the problem
- Design of the solution
- Developing the solution
- Evaluation
The aims of this qualification are to enable learners to develop:
- An understanding and ability to apply the fundamental principles and concepts of computer science, including: abstraction, decomposition, logic, algorithms and data representation.
- The ability to analyse problems in computational terms through practical experience of solving such problems, including writing programs to do so
- The capacity to think creatively, innovatively, analytically, logically and critically
- The capacity to see relationships between different aspects of computer science
- Core mathematical skills.
CURRICULUM OVERVIEW
The course breaks down into the following areas:
- Computer Systems: Learn how computers work, including hardware, software, and operating systems
- Algorithms and Programming: Develop programming skills in Python, Java, or similar languages and explore how algorithms solve problems.
- Data Representation: Understand how computers represent data in binary and use that knowledge to work with files, images, and sound.
- Networking and Cybersecurity: Study how networks operate, how data is transmitted, and the principles of securing systems.
- Problem-Solving & Software Development: Work on practical coding challenges and develop your own software applications.
Assessment of the course breaks down as follows:
- Component 1: Computer Systems (40%): A written exam covering hardware, software, networking, and cybersecurity.
- Component 2: Algorithms and Programming (40%): A written exam on algorithms, data structures, and programming concepts.
- Component 3: Non-Exam Assessment (NEA) (20%): A practical project where you’ll design, develop, and test a software solution to a real-world problem. Exams are held at the end of the two-year course. The NEA is a project-based assessment that takes place during the second year.
WHY STUDY COMPUTER SCIENCE?
There is a high demand for Computer Science skills. Many industries are looking for skilled computer scientists to drive innovation. The subject offers a wide range of career opportunities with fields such as software development, cybersecurity, artificial intelligence, and data analysis are booming.
It provides an excellent foundation for Higher Education, especially for further studies in computer science, engineering, and related fields.
Students can move on to engage in an innovative career field, becoming a part of shaping the future, from developing new technologies to solving global challenges.
This course will help students to understand how technology works in the real world, and enhances their problem-solving skills. It improves critical thinking and problem-solving abilities: The course teaches logical thinking, creative problem-solving, and how to break down complex tasks into manageable parts.
A computer science career progression typically involves starting as a developer or analyst, gaining experience, and then moving into roles like senior developer, team lead, UX or UI architect, or specialising in areas like cybersecurity, data science, or AI.
