2.1 Algorithms

2.1 Algorithms

1. Computational thinking

   • the use of computers to solve problems

2. Algorithmic Thinking

   • identifying the steps involved in solving a problem

3. Linear Search

   • each item in the list is checked in order
   • The worst case scenario is you have to check out all the values
   • The algorithm is simpler to use

4. Binary search

   • an ordered list is divided into 2 with each comparison
     • The worst case scenario that it only checks half of the value
   • The algorithm is longer and more complex to use

5. Decomposition

   • Breaking down a large problem into smaller sub-problems
     • they can be tested independently
     • There is not always one right answer.

6. Abstraction

   • Representing 'real world' problems in a computer using variables and symbols
     • also removes unnecessary elements from the problem

7. Bubble sort

   • moving through a list repeatedly, swapping elements that are in the wrong order

8. Merging Lists

   • Split all elements into individual lists
     • Compare the first element in both lists
       • Put the smallest into a new list
         • Compare the next element of 1 list with the second element of the 2nd list
           • Put the smallest into a new list
             • Repeat until merged

9. Insertion sort

   • each items is taken in turn, compared to the items already in a list and placed in a correct position

10. Ordered list

    • Elements are arranged in sequence

11. Flowchart

    • They can be used to represent an algorithm
      • Shows the data that is input and output
      • Shows the processes that take place
      • Shows the decisions and repetition that takes place

12. Pseudocode

    • Means 'fake code'
      • It's a partway between English language and programming code

13. Dry run

    • running through an algorithm with sample data, running each step manually

14. Trace table

    • a table that follows the values of variables to check for accuracy