Key term
Eduqas A-Level Computer Science A500QS
Data Structures: Arrays, Records and Hash Tables
Stacks, Queues, Linked Lists and Trees
Boolean Logic and Simplification
Algorithms, Pseudo-Code and Programming Constructs
Variables, Scope, Parameters and Identifiers
Recursion and Modular Programming
Sorting Algorithms
Searching and Shortest-Path Algorithms
Big O Notation and Algorithm Efficiency
Validation, Verification and Testing
Data Compression
Programming Paradigms and Computer Languages
Topic overviewProgramming paradigms and where each is usedProcedural programmingEvent-driven and visual programmingMark-up languagesObject-oriented programming: objects, classes and methodsAmbiguity in natural language and the standardisation of languagesBackus-Naur form and syntax diagramsHigh-level and low-level languages, and choosing one
Systems Analysis, Maintenance and Documentation
Topic overviewWaterfall, Agile and approaches to systems developmentThe feasibility study, budgets and time scalesMethods of investigationAbstraction, decomposition and data flow diagramsSelecting suitable hardware and softwareAcceptance testing and methods of changeoverPerfective, adaptive and corrective maintenanceBackup and recovery proceduresUser and maintenance documentation
System Design and Human-Computer Interaction
Software Engineering Tools and Translators
Topic overviewSoftware tools for the software engineering processSoftware packages in analysis, specification, design and testingIntegrated development environmentsDebugging toolsProgram version managementCompilers, interpreters and assemblersThe stages of compilationTranslation errors and execution errors
Computing, Society and the Law
Computer Architecture, the Processor and Assembly Language
Topic overviewHardware elements and how they are connectedVon Neumann architecture and the stored program conceptThe processor: control unit, ALU and registersTypes of memory: RAM, ROM and virtual memoryCache memory and locality of referenceThe fetch-execute cycle and its registersParallel processing and the limits to parallelisationAssembly language and executing a program
Input, Output and Secondary Storage
Topic overviewInput devices and how data is capturedOutput devices and how results are presentedChoosing input and output devices for a situationVoice input, dictation and voice print recognitionMagnetic, optical and solid state storageComparing secondary storage for a purposeDisc organisation, fragmentation and defragmentation
Networks, Protocols and the Internet
Topic overviewNetworks, LANs and WANsNetwork topologies and connection hardwareNetworking standards and the layered modelIP addressing, TCP and UDPApplication protocols: HTTP, FTP, SMTP, IMAP and DHCPHandshaking and establishing a connectionWireless hardware and wireless technologiesThe internet and connected mobile devices
Data Transmission and Network Performance
Topic overviewSerial and parallel transmissionSimplex, half duplex and full duplexMultiplexing and sharing a channelCircuit, message and packet switchingPackets, headers and packet contentsNetwork collisions and collision detectionRouting traffic on a networkCalculating transfer rates and lowest cost routes
Number Systems and Binary Arithmetic
Data Types, Characters and Floating Point
Topic overviewPrimitive data typesStorage requirements for each data typeASCII and standardised character setsUnicode and multilingual encodingFloating point form: mantissa and exponentConverting between real numbers and floating pointInteger and floating point comparedTruncation, rounding and accuracy
Files, Records and File Organisation
Topic overviewThe purpose of files in data processingFiles, records and fieldsFixed and variable length fields and recordsMaster files and transaction filesSerial and sequential file accessIndexed sequential access and multi-level indexesDirect access and hashing algorithmsOverflow handling and file reorganisationFile security, backups, generations and archiving
Databases, Normalisation and Entity Modelling
SQL, Big Data and Distributed Systems
Topic overviewQuery languages and the purpose of SQLCreating tables: CREATE TABLE, data types and keysRetrieving data with SELECT, FROM and WHEREComparison operators, IN, AND, OR and sub-queriesSorting, grouping and aggregate functionsInserting, updating, deleting and joining dataThe DBMS, the data dictionary and distributed databasesBig Data, data warehousing, data mining and predictive analytics
The Operating System and Its Modes
Topic overviewThe need for an operating system and the role of the kernelManaging peripherals, processes and memory protectionThe user interface and the layers between user and hardwareDirectory structures and file attributesUtility softwareBatch processing, real time control and real time transaction systemsTypes of operating system: single-user, multi-user, multi-tasking and multi-programmingDesigning an interface for different users and environments
Interrupts, Memory Management and Scheduling
Topic overviewWhat causes an interruptInterrupt handling and the interrupt service routineInterrupt priorities and how they are allocatedPartitioning main memoryMethods of data transfer between processor and devicesBuffers and double bufferingHigh level scheduling, processor and device allocationProcess states, time-slicing, polling and threading
Types of Software System
Data Security, Cryptography and Attack
Topic overviewOnline updating and the integrity of shared filesDangers of holding personal dataClerical procedures, access levels, passwords and write protectionCryptography: its purpose, and symmetric and asymmetric keysHashing, digital signatures and certificatesCaesar and Vernam ciphers and comparing cipher strengthBiometric technologies and their limitationsThreats, damage and contingency planningMalicious software, hacking and penetration testing
Data Compression
- 7 subtopics
- 21 things to know
- Component 1
What Data Compression covers
Data Compression is examined in Component 1, Programming and System Development. It covers what data compression is and why it is used, lossless compression, lossy compression, run-length encoding, dictionary and Huffman coding, measuring compression: ratio and saving percentage and choosing a compression method.
The 7 subtopics
Each one has its own page.
- What data compression is and why it is usedData compression, two reasons a file is compressed before storage and two reasons a file is compressed before transmission.Open →
- Lossless compressionLossless compression and two file types that must be compressed losslessly.Open →
- Lossy compressionLossy compression, two media types normally compressed lossily and lossy image format and a lossy audio format.Open →
- Run-length encodingHow run-length encoding works, data does run-length encoding compress well and data does run-length encoding compress badly.Open →
- Dictionary and Huffman codingPrinciple of dictionary compression, widely used dictionary compression algorithm and principle of Huffman coding.Open →
- Measuring compression: ratio and saving percentageFormula for compression ratio, formula for saving percentage and compression time.Open →
- Choosing a compression methodFirst question to ask when choosing a compression method and one factor besides ratio and time when choosing a format.Open →
What you need to know
Answer each one before you reveal it.
Your recallNot started
Key term
Why does random data resist compression, and where is lossless compression essential?
Key term
Define lossy compression.
Key term
Explain how run-length encoding works.
Key term
Explain the principle of dictionary compression.
Numbers
State the formula for compression ratio.
Key term
State the first question to ask when choosing a compression method.
Key term
Define data compression.
Key term
Define lossless compression.
Key term
Name two media types normally compressed lossily.
Key term
Is run-length encoding lossless or lossy?
Key term
Name a widely used dictionary compression algorithm.
Key term
A 200 MB file compresses to 50 MB. State the compression ratio.
Key term
Which method suits a scanned black-and-white document, and why?
Key term
State two reasons a file is compressed before storage.
Key term
State two file types that must be compressed losslessly.
Key term
Name a lossy image format and a lossy audio format.
Key term
Encode the string AAAABBBCCD using run-length encoding.
Key term
Why does a dictionary method suit natural language text?
Numbers
State the formula for saving percentage.
Key term
Which method suits a photograph for a web page, and why?
Take Data Compression further
The same content, four other ways.
Commonly confused
Pairs that lose marks when they are blurred together.
Compression ratio vs Saving percentage
Compression ratio: dividing the original size by the compressed size. Saving percentage: stating the proportion of the original removed.
Run-length encoding vs Huffman coding
Run-length encoding: replacing a run of identical values with a count. Huffman coding: giving frequent symbols shorter codes.
Dictionary compression vs Huffman coding
Dictionary compression: replacing a repeated sequence with a reference. Huffman coding: shortening individual symbols by frequency.
Questions from this topic
Each one with the model answer underneath.
- 1
Define data compression.
Model answer
Data compression is the process of re-encoding a file so that it occupies fewer bits than the original. The encoding removes redundancy rather than meaning.
- 2
State two reasons a file is compressed before storage.
Model answer
Compression lets more files fit in the same storage. The cost of that storage falls in proportion.
- 3
State two reasons a file is compressed before transmission.
Model answer
A smaller file takes less time to send over a given bandwidth. Less of the network's capacity is occupied while it travels.
- 4
What is redundancy in a data file?
Model answer
Redundancy is any part of a file that carries no information a decoder needs. Repeated bytes and predictable patterns are both forms of redundancy.
- 5
Explain why an uncompressed bitmap contains a great deal of redundancy.
Model answer
An uncompressed bitmap stores a full colour value for every pixel. Large areas of one colour therefore repeat the same value thousands of times.
- 6
What is a codec?
Model answer
A codec is the pair of algorithms that compresses data and decompresses it again. The name is short for coder-decoder.
What is inside Levo
Six ways to work the same material.
Data Compression revision questions
- What does Data Compression cover in Eduqas A-Level Computer Science?
- 7 subtopics: what data compression is and why it is used, lossless compression, lossy compression, run-length encoding, dictionary and huffman coding, measuring compression: ratio and saving percentage and choosing a compression method. Each one has its own page with questions and model answers.
- How is Data Compression organised?
- By the Eduqas A500QS specification, subtopic by subtopic, so it matches the order you are taught it in.
Every question in Data Compression, on a schedule.
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