Number Systems
Convert confidently between binary, denary and hexadecimal; understand binary addition, overflow and logical shifts.
Clear, structured notes for every chapter — perfect for revision and quick reference. Pick a chapter below, choose a topic, and jump straight into the content.
Theory — written exam on theory topics
Practical focus — problem solving and coding
Convert confidently between binary, denary and hexadecimal; understand binary addition, overflow and logical shifts.
How characters, sound and pictures are represented: character sets, sample rates, resolutions, bit depths, and file compression basics.
Calculate storage needs for text, images and sound; storage units like KiB, MiB, GiB, TiB; lossy vs lossless compression methods.
Data packets and packet switching; serial & parallel, simplex, half-duplex and full-duplex; the USB interface.
Parity checks (even/odd), checksums, check digits, and ARQ — how errors are detected in transmitted data.
Symmetric and asymmetric encryption techniques, and the differences between them.
The Fetch–Decode–Execute cycle, CPU components and registers, and the Von Neumann architecture.
Scanners, microphones, screens, speakers, printers — how input and output devices are used and connect.
Physical, magnetic and optical storage; RAM and ROM; how storage media are used and their characteristics.
Routers, network interface cards (NICs), and MAC and IP addresses — how data gets routed.
Operating systems and application software, and how interrupts are handled by the OS.
High-level vs low-level languages; assemblers, compilers and interpreters; features of the IDE.
The hardware and software behind the internet; browsers, web servers, HTTP/HTTPS and URLs.
How digital currency payments work, and the advantages and disadvantages of online currency.
Phishing, pharming and other security threats; prevention with biometrics, two-factor authentication and more.
Sensors, microprocessors and actuators working together in automated feedback loops.
Characteristics, components and roles of robots in industry and everyday life.
AI characteristics, machine learning, and expert systems — how intelligent systems are built and used.
Analyse, design, code and test — the phases of the program development life cycle.
How systems are broken into sub-systems and decomposed for structured problem solving.
Reading and interpreting given algorithms and expressing their purpose in plain words.
Linear search, bubble sort, totals, counting, maxima and minima — the classic solution routines.
Data validation types (range, length, type, presence, format) and verification methods.
Normal, abnormal, extreme and boundary test cases and why each matters.
Trace tables and dry runs to track variable changes and follow algorithm logic step by step.
Spotting syntax errors, logic errors, and correcting them in written algorithms.
Matching algorithms to given problems, writing pseudocode and amending existing solutions.
Variables, constants, data types, sequences, selection and iteration — the building blocks of programs.
Declaring, populating and manipulating one- and two-dimensional arrays.
Opening, reading, writing and closing data files; storing and retrieving records.
Physical vs logical databases, single-table structures, records, fields, validation and verifying data.
AND, OR, NOT, NAND, NOR and XOR gates and their truth tables; building and completing logic circuits.