programming

programming

1. computational thinking

   • abstraction
     • abstraction allows us to simplify a given problem by removing unnecessary deatil so that you can focus on the important parts that are relevant to the problem
   • decomposition
     • decomposition helps us solve problems, which means breaking a complex problem into smaller, more manageable sub-problems. Each smaller parts can be solved individually

2. search algorithms

   • binary search
   • linear search
     • in linear search, each item is compared one by one, starting from the start of the dataset.
     • the best case scenario for linear search is if the item we ae looking for is at the start of the dataset
     • the worst case scenario is if it checks the whole dataset and the item we are looking for is not even part of th dataset
     • pros of linear search: the dataset does not need to be sorted in order for it to work,
       • cons of linear search: it is very slow and not efficient.

3. sorting algorithms

   • merge sort
   • bubble sort
   • insertion sort

4. pseudocode

   • pseudocode is a common way of representing an algorithm. it is not an actual programming language like python or java, it is rather used to plan algorithms, focusing on the logic of the steps.

5. flowcharts

   • flow diagrams are used to visually represent the steps that make up an algorithm. a standard set of shapes are used to represent a certain step.
     • oval- starts or terminates a flowchart.
       • rectangle- the user performs a task
         • diamond- a decision is made
           • parallelogram- the user inputs/outputs data
             • rectangle with 3 boxes inside it- an extra flowchart called upon in the main diagram

6. algorithms

   • an algorithm is a reasonable set of instructions (a set of steps) to solve a given problem.