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Pearson Edexcel GCSE (9-1) Computer Science revision notes

Data storage and compression Revision Notes

Review revision notes for Data storage and compression in Edexcel Computer Science.

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Data storage and compression

Pearson EdexcelGCSE (9-1)Computer SciencePaper 1 (1CP2/01)

Revision notes

  • Understanding Binary Data Storage Units Revision Notes

    Binary Data Storage Units

    Data in computing is stored using binary. The smallest unit is a bit (0 or 1). Key units for data storage are:

    • Bit: A single binary digit (0 or 1).
    • Nibble: 4 bits.
    • Byte (B): 8 bits.
    • Kibibyte (KiB): 1024 Bytes (2^10 Bytes).
    • Mebibyte (MiB): 1024 KiB (2^10 KiB).
    • Gibibyte (GiB): 1024 MiB (2^10 MiB).
    • Tebibyte (TiB): 1024 GiB (2^10 GiB).

    Calculating File Sizes and Capacity

    To calculate file sizes or data capacity, you often need to convert between these units. For example, to find out how many bytes are in a 5 MiB file, you would multiply 5 by 1024 (for KiB) and then by 1024 again (for Bytes). Always remember that these units use powers of 2 (1024), not powers of 10 (1000) as in the metric system, which is common for drive manufacturers but not precise data storage.

  • Understanding Data Compression and Its Methods Revision Notes

    Why Data Compression is Necessary

    Data compression is essential for managing digital information efficiently. It addresses storage limitations by reducing file sizes, allowing more data to be stored on the same hardware. This saves costs associated with storage infrastructure. Additionally, compression improves data transmission speeds over networks by requiring less bandwidth to send and receive files, leading to faster downloads, smoother streaming, and more responsive online experiences. Without compression, sharing and storing the vast amounts of data generated daily would be significantly more challenging and costly.

    Types of Data Compression: Lossless vs. Lossy

    There are two main types of data compression. Lossless compression removes redundant data in a way that allows the original data to be perfectly reconstructed upon decompression. This is critical for data integrity where no information can be lost, such as text documents, spreadsheets, and program files (e.g., ZIP, PNG, GIF). Lossy compression, conversely, achieves higher compression ratios by permanently removing some data that is considered less critical or imperceptible, particularly in multimedia. While some quality is lost, it's often an acceptable trade-off for significantly smaller file sizes, common for images (JPEG), audio (MP3), and video (MPEG).

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