By Why Data Structures is Important for Algorithms?
When solving a problem, an algorithm breaks down the problem step by step based on the data provided. It then applies methods like searching, sorting, or other techniques to solve it. The relationship between algorithms and data structures is crucial. If data is well-organized, it becomes easier to retrieve, add, or manipulate it, which is the core function of the algorithm.
While algorithms can work on unstructured data, the process will be slower and less efficient. For example:
Unstructured Data:
Structured Data:
In structured data, RAM speed and space are better utilized.
Classification of Data Structures
There are two main types of data structures:
1. Primitive Data Structures
- Numerical
- Character
- Logical
- Pointer
2. Non-Primitive Data Structures
- Array
- Linked List
- Stack
- Queue
- Tree
- Graph
- Dictionary
- File
Understanding Algorithms
Since we are frequently discussing algorithms, it is important to elaborate on them. An algorithm is simply a method for solving problems. When you start solving a problem, you mentally plan each step—this is essentially what an algorithm does. It works step by step to achieve the desired outcome.
Pseudocode:
Algorithms are often written in pseudocode, breaking the solution into a few simple steps that can be understood in any language. There are no fixed rules for writing pseudocode—simplicity is key.
There’s also a flowchart, which provides a visual representation of an algorithm with symbols to represent each step.
Flowchart:
Flowcharts are another way to represent algorithms, primarily for IT professionals. Each step of the algorithm has a unique symbol or shape, helping visualize how to solve the problem.
