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3.3 Fundamentals of data representation

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3.3Fundamentals of data representation
3.3.1Number bases
 R36 I can understand decimal (base 10)
 R36 I can understand binary (base 2)
 R43 I can understand hexadecimal (base 16).
 R36 I can understand that computers use binary to represent all data and instructions.
 R43 I can explain why hexadecimal is often used in computer science.
3.3.2Converting between number bases
 R36 I can understand how binary can be used to represent whole numbers.
 R43 I can understand how hexadecimal can be used to represent whole numbers.
 R37 I can convert from binary to decimal.
 R38 I can convert from decimal to binary.
 R43 I can convert in both directions between binary and hexadecimal.
 R41 I can convert from hexadecimal to decimal.
 R42 I can convert from decimal to hexadecimal.
3.3.3Units of information
 R45 I can explain that that quantities of bytes can be described using prefixes.
 R45 I can explain that kilo, 1 KB is 1,000 bytes.
 R45 I can explain that mega, 1 MB is 1,000 kilobytes
 R45 I can explain that giga, 1 GB is 1,000 Megabytes
 R45 I can explain that tera, 1 TB is 1,000 Gigabytes.
 R45 I can compare quantities of bytes using the prefixes above
3.3.4Binary arithmetic
 R39 I can add together two binary numbers.
 R40 I can add together up to three binary numbers.
 R44 I can apply a binary shift to a binary number.
 R44 I can describe situations where binary shifts can be used.
3.3.5Character encoding
 R46 I can understand what a character set is.
 R46 I can describe the ASCII amd Unicode character encoding methods:
 R46 I can convert characters to character codes
 R46 I can convert character codes to characters.
 R46 I can understand that character codes are commonly grouped and run in sequence within encoding tables.
 R47 I can describe the purpose of Unicode and the advantages of Unicode over ASCII.
 R47 I can describe how Unicode uses the same codes as ASCII up to 127.
3.3.6Representing images
 R48 I can explain what a pixel is and how pixels relate to an image and the way images are displayed.
 R48 I can describe for bitmaps the image size in pixels.
 R48 I can describe for bitmaps the colour depth.
 R49 I can describe how the size of a bitmap image is measured in pixels (width x height)
 R49 I can describe how a bitmap represents an image using pixels and colour depth.
 R49 I can explain how the number of pixels and colour depth can affect the file size of a bitmap image.
 R49 I can calculate bitmap image file sizes based on the number of pixels and colour depth.
 R49 Convert binary data into a bitmap image.
 R49 I can convert a black and white image into binary data.
3.3.7Representing sound
 R50 I can understand that sound is analogue and that it must be converted to a digital form for storage and processing in a computer.
 R50 I can understand that analogue signals are sampled to create the digital version of sound.
 R50 I can describe the digital representation of sound in terms of sampling rate.
 R50 I can describe the digital representation of sound in terms of sample resolution.
 R50 I can calculate sound file sizes based on the sampling rate and the sample resolution.
3.3.8Data compression
 R51 I can explain what data compression is.
 R51 I can understand why data may be compressed and that there are different ways to compress data.
 R53 I can explain how data can be compressed using Huffman coding.
 R53 I can interpretHuffman trees.
 R53 I can calculate the number of bits required to store a piece of data compressed using Huffman coding.
 R53 I can calculate the number of bits required to store a piece of uncompressed data in ASCII.
 R52 I can explain how data can be compressed using run length encoding (RLE).
 R52 I can represent data in RLE frequency/data pairs.


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Teacher Date: 28-09-21


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