GCSE Computer Science Edexcel

This subject is broken down into 82 topics in 6 modules:

  1. Problem Solving 14 topics
  2. Programming 15 topics
  3. Data 17 topics
  4. Computers 18 topics
  5. Communication and the Internet 13 topics
  6. The Bigger Picture 5 topics
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This page was last modified on 28 September 2024.

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Computer Science

Problem Solving

Algorithms

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Algorithms

Understanding Algorithms

  • An algorithm is a precise list of instructions to complete a task. It’s like a recipe for a computer.

  • Algorithms can be used for a wide range of tasks and are crucial in problem solving.

  • They should be specific (clear and detailed), unambiguous (no room for varied interpretation), and effective (they work!).

  • Algorithms can be written in natural language, pseudocode, or flow charts.

Creating Algorithms

  • All algorithms involve a process of problem decomposition – breaking a problem into smaller, manageable parts.

  • Algorithms need inputs (data to process), processes (what to do with the data), and outputs (the result).

  • Creating algorithms involve logical thinking – you need to thoroughly understand the problem to develop a solution

Evaluating and Improving Algorithms

  • When evaluating an algorithm consider its correctness (does it produce the desired output?), and efficiency (how quickly and resourcefully does it solve the problem?).

  • An algorithm's complexity (measured in terms of time or space) can impact its real-world usability.

  • Algorithm's efficiency can be addressed by refining the algorithm – consider the steps, can anything be combined or removed?

  • Algorithms can be tested using trace tables to visually track what’s happening step by-step.

Types of Algorithms

  • There are numerous types, including: searching algorithms (finding a specific item in a list), sorting algorithms (organising items in a specific order), and computational algorithms (perform calculations).

  • Key searching algorithms include: linear search (checking every item in order), and binary search (repeatedly dividing the list in half).

  • Key sorting algorithms include: bubble sort (comparing adjacent items and swapping them if in wrong order), and merge sort (dividing list repeatedly into two halves until each sublist has one element, then merging them back in sorted order).

  • Computational algorithms could involve various math functions, such as calculating Fibonacci sequence or factorial of a number.

Course material for Computer Science, module Problem Solving, topic Algorithms

Computer Science

Data

Run-Length Encoding

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Run-Length Encoding

Understanding Run-Length Encoding

  • Run-Length Encoding (RLE) is a simple form of data compression in which consecutive elements which are the same (runs) are replaced by the element itself and the length of the run.
  • RLE is best suited for data with many such runs, and can thus reduce the size significantly.
  • The aim of using RLE is to decrease storage space, enhance data transfer speed and sometimes to anonymise data.

Concept of Run-Length Encoding

  • In this method, sequences of the same data values are replaced by a count number and the actual value.
  • For example, in a raster graphics image (rows of image pixel data), a sequence of the same colour value could be compressed using RLE: 'blue-blue-blue-red-red' becomes '3-blue-2-red'.
  • For monochromatic or simple bicolor images, like diagrams, black-and-white sketches, and icons, RLE is extremely efficient.

Application of Run-Length Encoding

  • RLE is not only used in images, but also in various data formats such as TIFF and BMP files.
  • It is also frequently used in sending and storing text data. For example, 'bbbbbaaaaa' will be stored as '5b5a' using RLE.
  • However, for complex images or with data sets where the same value changes frequently, RLE might not be the best choice.

Evaluation of Run-Length Encoding

  • Run-Length Encoding is an excellent option for specific types of data, but it's not a universal solution.
  • If the data doesn't have many consecutive repeat values, RLE can sometimes increase the size of the data because of the need to store the length values alongside unique data elements.
  • Therefore, it's very important to understand the nature of the data you're working with before deciding whether to use RLE.

Benefits and Limitations of Run-Length Encoding

  • The major advantage of using RLE is the simplicity of implementation, and it's also quick to encode or decode.
  • While RLE is excellent when dealing with specific types of data, it can significantly inflate data size when applied to the wrong type of data. Therefore, it's known for its situational advantages.
  • Selecting the right compression technique for the right type of data is crucial for optimal performance.

Course material for Computer Science, module Data, topic Run-Length Encoding

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