What is Data Structure and Algorithm?

Data Structure is the method of collecting and organizing data in an organized way. So, that we can perform different types of operations on these data in a very effective way. Data Structure is a way of rendering data elements in terms of some relationship. Data Structure and Algorithm is a process of better organization and storage of data elements. For example, we have some data which has student’s name with Roll no. Like a student named Rohit and his roll number is 26. Here “Rohit” is of String data type and 26 is of integer data type.

We can organize these data as a record lie student record, which will have both students’ names abc their roll number on it. Now we can collect and store the student’s record in a database or file as a data structure. For example “Rohit” 26, “Arman”5, “Sushil” 34.

If you know about the concepts of Object-Oriented Programming, then you are aware that class also does the same thing as a data structure. It collects different types of data and their values in one single entity. The only difference in the data structure is that it provides techniques to access and manipulate the data in a more effective way.

In simple language, we can say that Data Structures are programmed to store the data in an ordered way. Because of that, we can perform various operations on it in a very easy way. Data Structure represents the knowledge of data to organize in a memory location. Data should be designed and implemented in such a way that it increases its efficiency by reducing complexity.

Types of Data Structure:

As of now, we have discussed that we can call data structure to anything that can store data. That is why Integer, Float, Boolean, Char, etc., all are types of data structures. These are known as Primitive Data Structure. Now we have also some complex Data Structures that are used to store large and connected data. Some of the examples of Abstract Data structures are:

l Linked List

l Tree

l Graph

l Stack

l Queue

All these Data structures can be used to perform different types of operations on data. These data structures are selected on the basis of the types of operation which is to be performed.

The data structures are also classified on the basis of the following characteristics:

Linear data structure: In linear data structure the items are arranged in a linear sequence. Example: Array

Non-Linear Data structure: In a nonlinear data structure, the data items are not arranged in a linear sequence. Example: Tree, Graph

Homogenous data structure: In a Homogenous data structure all the items are of the same type. Example: Array

Non Homogenous data structure: In a Non Homogenous data structure all the items are not of the same type. They are different. Example: Structures

Static Data Structure: Static Data structures are those whose sizes and structure associated memory locations are fixed at compile time. For example — Array

Dynamic data structures: Dynamic data structures are those that shrink or expand depending upon the need of the program and its execution. There associated memory location also changes. Example- Liked list created using pointers.

What is an Algorithm?

An algorithm is a finite set of logic or instruction, written in order to perform a certain predefined task. An algorithm is not a complete program or code; it is only a logical solution to a problem. It can be expressed either as an informal high-level description using a flow chart.

Each algorithm must satisfy the following properties:

1. Input- There should be 0 or more inputs supplied externally to the algorithm.

2.Output- There should be at least 1 output obtained.

3. Definiteness- Every step of the algorithm should be clear and well defined.

4. Finiteness- The algorithm should have a finite number of steps.

5. Correctness- Every step of the algorithm must generate the correct output.

An algorithm is fast and efficient if it takes less execution time and takes less memory space. An algorithm’s performance is measured on the basis of the following properties:

1. Time Complexity

2. Space Complexity

Space Complexity

Space Complexity is the amount of memory space that is required by an algorithm during the course of its execution. For Multi-user systems, space complexity is a must. It is also must be taken seriously where limited memory is available.

An algorithm generally requires space for the following components:

1. Instruction Space: It is the space that is required to store the executable version of the program. Instruction space is fixed but can vary depending on the number of lines of code in the program.

2. Data Space: Data space is the space that is required to store all the variables and constants’ values. It also includes temporary variable values.

3. Environment Space: Environment space is the space required to store the environment information that is needed to resume the suspended function

Time Complexity

Time complexity is the method to represent the required amount of time needed by a program to run until its completion. It is generally good to try to keep the required time minimum. So that the algorithm completes its execution in the minimum possible time.

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