Computer Science

Computer Science develops the ability to think computationally, break complex problems into manageable parts, and design algorithmic solutions that can be implemented and tested in code.

Students learn the principles that underpin modern computing data representation, hardware and networks, cybersecurity, and the societal impact of technology alongside practical programming, focusing on writing clear, correct, and efficient solutions.

Throughout the course, students practice planning, coding, testing, debugging, and evaluating programs using industry-standard conventions (naming, structure, comments) and robust test strategies. They also build confidence with trace tables, dry-running, and reasoning about program behaviour before writing code.

1) Problem Solving & Algorithms

• Computational thinking: abstraction, decomposition, pattern recognition, generalisation
• Algorithm design: sequencing, selection, iteration (count- and condition-controlled loops)
• Pseudocode and flowcharts; tracing, testing strategies; understanding time/space considerations at a foundational level

2) Programming (Procedural focus)

• Core constructs: variables, constants, data types, operators, input/output
• Selection (IF/ELIF/ELSE), iteration (FOR/WHILE), nested structures
• Subroutines (functions/procedures), parameters, return values
• Data structures: 1D/2D arrays (lists), string handling, basic files (read/write)
• Defensive programming and validation; systematic testing & debugging

3) Data & Representation

• Number systems: binary, hexadecimal; conversions
• Data units (bit → byte → KB → MB → …); character sets (ASCII/Unicode)
• Images & sound: pixels, colour depth, resolution; sampling, sample rate/bit depth
• Compression: lossy vs lossless (purpose and impact)

4) Computer Systems & Networks

• Hardware: CPU components and the fetch-decode-execute cycle; memory hierarchy (registers, cache, RAM/ROM); storage (SSD/HDD/optical, capacity vs speed vs portability)
• Software: system vs application software; OS responsibilities; translators (interpreters/compilers/assemblers)
• Networks: LAN/WAN concepts, topologies, hardware (NIC, WAP, switch, router), IP/MAC addressing, the internet and DNS; client–server vs peer-to-peer; basic protocols; performance factors

5) Cybersecurity & The Bigger Picture

• Threats and vulnerabilities: malware, social engineering, brute force, SQL injection (conceptual)
• Authentication, authorisation, encryption, firewalls, secure backups, updates
• Legal, ethical, environmental and privacy considerations; impacts of emerging tech (AI/automation, cloud, data collection)

• Paper 1: Principles of Computer Science (theory) — 50%
  • Written exam on computational thinking, algorithms, data representation, computer systems, networks, cybersecurity, and societal issues
  • Question styles include multiple-choice, short answers, structured responses, trace-table/dry-run style tasks, and longer written explanations
• Paper 2: Application of Computational Thinking (practical/onscreen style) — 50%
  • Assesses algorithm design and programming skills (e.g., writing/reading/analysing code, completing/rewriting subroutines, producing correct outputs, and designing tests)
  • Students apply programming constructs, data structures, and testing methodology to solve specified problems

(Your department will confirm the programming language used for teaching commonly Python so students can practise consistently and develop fluency.)

• Builds powerful problem-solving habits and a structured approach to tackling complex tasks
• Develops code literacy and the ability to automate processes high-value skills across industries
• Enhances logical reasoning, resilience, and precision; complements Maths, the Sciences, and D&T
• Excellent preparation for A-level Computer Science and a wide range of technology pathways

• A-level Computer Science; Mathematics and Further Mathematics are strong companions
• Higher education and careers in Software Engineering, Data/AI, Cybersecurity, Games/Graphics, Networks/Cloud, DevOps, FinTech, and beyond
• Highly transferable skills for engineering, economics, business analytics, and any field using data and automation