Cambridge IGCSE Computer Science: learning guide

Cambridge IGCSE Computer Science asks you to do two things well: explain how computer systems work, and design, trace and test algorithms. It rewards careful reading, small tests and the habit of predicting what a program will do before you run it.

This guide gives you the shape of the subject, a study order through every module on this site, the syllabus-route checks you should make, and how to decide when extra help is worth having.

What is IGCSE Computer Science, and what do the codes mean?

The code used most on this site is 0478. Cambridge also lists 0265 for Computer Science. They are not treated here as interchangeable, and we do not claim that one replaces the other.

Cambridge publishes a 0478 syllabus document for the 2026 to 2028 exam years, and its 0265 page carries overview and notices. Treat this page as a map and the Cambridge pages as the authority for scope, papers and dates. Your school or exam centre controls entry, registration and results, and confirms which code and year apply to you.

Computer Science is also separate from ICT. If you want the practical-software subject instead, see IGCSE ICT. Use our syllabus and exam-year navigator to build a checking list, and read the 0478 guide, the 0265 guide and the page on the 2029 changes before you plan revision.

How is the subject built?

Think of Computer Science as two strands that support each other.

1. Computer systems: how data is represented, how hardware and software work, how networks move and protect data, and how automated systems behave.
2. Algorithms and programming: designing a method, writing it in pseudocode or code, tracing it, testing it with suitable data, and explaining it.

A typical lost mark sits where the strands meet. A student knows what an array is, but cannot say what the array holds on the second pass of a loop. The assessment guide shows how to find the papers and their weighting on the Cambridge page, because those details depend on your exam year.

In what order should I study the topics?

The order below starts with data, because later topics build on it. Then it moves to machines and networks, then to programming, and finally to explanation and route checking. Follow your school’s scheme of work where it differs, and use this list to fill gaps.

Start here: how data is stored and sent

1. Representing numbers and text: binary, hexadecimal and character codes. Every later topic uses these conversions.
2. Images, sound and storage: how file sizes are worked out from stated assumptions.
3. Data transmission and checking: packets, parity and checksums, and how errors are detected.

Machines, software and logic

4. Hardware and processing: the processor, memory, storage and the instruction cycle.
5. Software and systems: operating systems, translators, interrupts and embedded systems.
6. Boolean logic: gates, truth tables and logic statements. It links directly to how hardware makes decisions.

Networks, safety and automation

7. Internet, security and responsible use: URLs, browsers and servers, encryption ideas and defensive habits.
8. Automation and emerging systems: sensors, feedback, decisions and the limits of automated data.

Algorithms and programming

9. Algorithm design: decomposing a problem, flowcharts and trace tables. This module is the core of the subject.
10. Pseudocode for the applicable syllabus: the notation your paper uses, which varies by route.
11. Repetition and arrays: loops, stopping conditions and stored lists.
12. Programming structure: selection, procedures, functions and variables.
13. Validation, verification and testing: checking input, checking copying, and choosing test data.
14. Searching, sorting and files: step-by-step algorithms and reading and writing data.
15. Databases and query reasoning: tables, fields and combining SQL conditions.
16. Python transition and execution: moving from pseudocode to working code, and keeping advice for different exam years apart.

Across every topic

17. Problem-solving explanations: describing what an algorithm achieves in words. Work on this alongside the others.
18. Digital versus paper route checking: how to check with your exam centre whether your route is digital or on paper, and how to practise for it.

What are the most common difficulties?

Students usually report one of these. Each has a short guide with original examples.

• A loop that never ends or skips the final item: loops that run forever or miss the last item.
• A program that works on samples and fails at the edges: boundary cases.
• Mixing up validation and verification: validation versus verification.
• An SQL query that combines conditions the wrong way: SQL conditions.
• Following Python-only advice written for another exam year: using the right route’s advice.
• Knowing the code but not being able to describe the algorithm: explaining without the finished code.

The restricted pseudocode trace trainer and the safe Python reasoning sandbox let you practise tracing and prediction without setting up software.

An orienting example: one trace table

Here is a short algorithm. Predict the output before reading on.

Total ← 0
FOR i ← 1 TO 4
    Total ← Total + i * 2
NEXT i
OUTPUT Total

Trace it. On the four passes, i * 2 is 2, 4, 6 and 8, so Total goes 2, 6, 12, 20. The output is 20.

Now check it another way: the sum of 1 to 4 is 10, and doubling gives 20. Two routes agree. That habit of predicting, tracing and checking is the skill the whole subject uses.

How should I study Computer Science well?

A routine that works is short and repeatable. Do it after each topic.

1. Write the meaning of each key term in one line, then give a small example of it.
2. Predict before you run. Write what you expect a program or trace to output.
3. Trace by hand with a table of variables, one row per pass.
4. Test the edges: an empty list, one item, the largest allowed value, and two equal values.
5. Explain in a sentence what the algorithm is meant to achieve, without quoting the code.
6. Keep an error log with three columns: what I did, what went wrong, what I will check next time.

Use the original practice section for mixed questions with explained answers, the terminology guide for words that are easy to confuse, and the revision guide to plan around real gaps. The study route guide helps you build a plan from the right syllabus.

What does one-to-one teaching add?

Reading explains what is correct, and running code shows what happens. A teacher watches how you reason about a problem, and can stop you at the moment you assume a variable holds something it does not. That correction is hard to get from a book, because the book cannot see your working.

If you want to try it, online one-to-one Computer Science tuition starts with a paid one-hour trial at the assigned teacher’s confirmed rate, from RM80. If you are not sure yet, considering Computer Science tuition explains when self-study or group classes are enough.