2.6 - Data Representation

Sam Haynes
Mind Map by Sam Haynes, updated more than 1 year ago
Sam Haynes
Created by Sam Haynes almost 3 years ago
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Mind Map on 2.6 - Data Representation, created by Sam Haynes on 03/16/2019.

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2.6 - Data Representation
  1. How is data represented?
    1. Bit = 0 or 1.
      1. Nibble = 4 bits.
        1. Byte = 8 bits/ 2 nibbles.
          1. Kilobyte = 1000 bits.
            1. Megabyte = 1000 KB
              1. Gigabyte - 1000 MB
                1. Terabyte = 1000 GB
                  1. Petabyte = 1000 TB
                  2. Denary --> Hex
                    1. Divide by 16 - Whole number = the first digit, the remainder = second digit.
                      1. 167/16 = 10.7
                        1. 10 = A, 7
                          1. Therefore 167 = A7
                    2. Binary ---> Hex
                      1. Split the byte into 2 nibbles.
                        1. Add up each number per nibble and get 2 digits. This is your answer.
                        2. You would tend to use hex because:
                          1. Has a shorter string so uses less storage.
                            1. Easily converted to binary if needed.
                              1. Programmers find it easier to work with.
                            2. Characters
                              1. ASCII has 7 bits.
                                1. Extended ASCII has 8 bits.
                                  1. 256 possible characters.
                                  2. Unicode has 2 bytes.
                                    1. giving 2^16 possibilities (65,536).
                                    2. To get the lower case version of a capital letter in binary, just add 32.
                                    3. Images
                                      1. Bitmap
                                        1. Becomes blurred when you zoom in because each pixel is assigned a colour.
                                          1. The page is divided into an invisible grid.
                                            1. Every bit is mapped.
                                            2. Vector
                                              1. Follows a set of mathematical instructions.
                                                1. For example:
                                                  1. Draw a circle, radius = 6 pixels, centre(10,10), line thickness =1 pixel.
                                                  2. Doesn't blur when zoomed in.
                                                  3. Each image holds 'metadata' - data about data.
                                                    1. Resolution - the number of pixels used.
                                                      1. The higher the resolution, the greater the quality.
                                                        1. The greater the resolution, the larger the size.
                                                      2. Compression
                                                        1. Saves space, reduces the amount of data transferred and runs quicker.
                                                          1. Lossless
                                                            1. No data lost, the reconstructed file is identical to the original and not all files can be compressed like this.
                                                            2. Lossy
                                                              1. Loses some info, acceptable to do this with images and vide0, but not text.
                                                            3. Audio
                                                              1. Sample frequency - the number of captured samples per second.
                                                                1. Sample size/Sample depth - the number of bits available per second.
                                                                  1. Bit rate - the number of bits used per second.
                                                                    1. More samples lead to better quality but larger file size.
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