Explore how computer systems store, manipulate, and convert various data types like text, images, and videos through integrated software and hardware. Gain insights into accessible data representation.

Computer systems do not understand human instructions, nor can they perceive real-life data in its raw form. Therefore, computer systems require a way to store and represent information in an accessible way. They use software and hardware components combined to help retrieve information, store it, manipulate it, and convert it back to a human-accessible format. If you want to learn how computer systems perform complex tasks such as storing and manipulating textual data, videos, music, images of your cat, and much more, this course is meant for you.

Information Representation in Computer Systems

The Huffman Encoding algorithm can be used to find provably optimal length prefix free codes to compress data that involves redundancies. For simplicity, the algorithm can be divided into five parts:

1. First, we create a #key# dictionary: A Python dictionary stores unordered key-value pairs. #key# that records the frequency of each character in the input text in line 20 inside the function `createFrequencyDictionary`. This dictionary is called `frequencyDictionary`. We check for every unique character and add its count in the `frequencyDictionary` against the character. 

2. Next, we create a #key# min-heap: A min-heap is a data structure that enables easy access to the smallest element from within a collection because the smallest element is at the root. #key# in line 30 inside the function `createMinHeap` that takes each entry from the `frequencyDictionary` and adds it as a #key# `node`: A user-built object with four attributes in this case. #key# in the Huffman Tree. A min-heap is useful for the Huffman algorithm because we need to access the two smallest frequency elements repeatedly. Therefore, we take all the characters and their frequencies and insert this data into a min-heap.

3. Next, we take two nodes with the smallest frequencies from the min-heap and merge them by adding their respective frequencies into a new Node in the function `createTree`. Here, we also set the left and right of the merged node to the original two nodes, respectively in line 45 and 46 and add the merged node back into the min-heap. We repeat this step until only one node is remaining, which is also referred to as the `root`.

4. Once the Huffman Tree is built, we traverse the tree, depth first, whereby assigning the code 0 to the left subtree and 1 to the right subtree, shown on lines 55 to 57. 

5. The last step involves encoding the data with the new binary codes that are assigned to each character, as shown in lines 63 and 64. 

Upon running the code, you can also visualize the number of bytes required by the original string and the compressed string along with the average number of bits required for each character.

In the code snippet below, try various strings as input to the program to observe that the codes for each character and the bits per character keep changing.

In this lesson, you will understand the details of Huffman Encoding with the help of an exercise on text compression.

Computer basics

Hardware and Software Components of a Computer

Data Representation in Python

Image, Audio, and Video Representation in Python

Data Compression

Text Compression