# Data Structures & Algorithms

Level : Beginner
Mentor: Shailendra Chauhan
Duration : 00:05:00

## What are Data Structures?

Data Structure is a method of organizing and storing data in memory. It makes it easier to access and manipulate data, allowing you to acquire the result you want in a short period.

## Why are Data Structures Important?

Data structures are important for the following reasons:

• Data management is efficient.
• Organization of Data.
• Abstraction of Data.
• Reusability.
• Optimizing an algorithm.

## Data structure Types

There are two main types of data structures:

1. Linear Data Structure
2. Non-linear Data Structure

## Linear Data Structure

Each element in a linear data structure is connected to the ones next to it in a sequential arrangement. Static structures, such as arrays, have fixed memory, whereas dynamic structures, such as stacks and queues, modify during runtime, improving memory efficiency. They are suitable for application software because they allow for single-run traversals, but their complexity increases with data size.

## Non-linear Data Structure

Non-linear data structures, such as trees and graphs, organize components hierarchically rather than sequentially. With pieces at varying levels, they cannot be traversed in a single run, making them useful for applications such as image processing and AI while retaining consistent time complexity.

• Language independence.
• Improves logic building.
• Improves the problem-solving method.
• Enables you to solve real-world challenges.
• Helps with code optimization.
• Increases adaptability to new technology stacks.
• Helps you acquire positions at top tech companies (MAANG).

• Complicated data structures are difficult to develop and implement.
• Flexibility and scalability are limited.
• Debugging and testing are complex.
• Existing data structures are difficult to alter.

## Operations of Data Structure

• Insertion: Add a new element to a data structure.
• Deletion: Removes an existing element from a data structure.
• Searching: Locate an element within a data structure.
• Sorting: Arranging the elements of a data structure in a certain order.
• Traversal: Visit each element of a data structure exactly once.
• Merging: Merging is the combination of two similar data structures.

## Data Structure Basic Terminologies

Basic Terminologies of Data Structure includes:

• Data
• Database
• Elementary Items
• Entity
• Attributes

Abstract data types are data structures that outline the declaration of data and actions but do not specify how these operations are performed. Examples of ADT include arrays, linked lists, stacks, queues, binary trees, and graphs. The programming language you choose will determine how ADT is implemented.

## What is an Algorithm?

An algorithm is a set of rules or a method with steps that is followed in a precise order to get the desired result in a given issue. The most efficient algorithm is one that has a minimum of steps to achieve the desired output. The most efficient algorithm is one that requires the least amount of time and space to perform.

### Criteria for Algorithm Evaluation

There are two primary criteria for judging or analyzing an algorithm:

1. Effectiveness: how many steps will it take to achieve the intended result?
2. Efficiency: How much time and memory will it require to execute?

## Features of a Good Algorithm

• Input Requirement: All algorithms require input.
• Output Clarity: Clear outputs are necessary.
• Finiteness: Algorithms must have a set number of steps.
• Unambiguous Instructions: Instructions should be clear and concise.
• Effectiveness: Each step must efficiently generate the desired result.
• Language Neutrality: Algorithms must be independent of programming languages.

## An algorithm's data flow

• Problem Statement: Describes the problem, available resources, and action plan.
• Algorithm: A collection of instructions produced from problem analysis.
• Input: Required inputs for the algorithm.
• Processing Unit: Executes instructions to generate output.
• Output: The program's result or outcome.

• Help with understanding processes, inputs, & results.
• Simplify difficult operations for programmers.
• Increase expandability by examining real-world relevance.
• Maintain programming efficiency.
• Enable simple debugging and error detection.
• Optimize the memory space.
• Provide various efficient answers to challenges.

• Algorithms are not designed to solve complex problems.
• Algorithms may take a long time.
• Expressing repetitive operations, conditional statements and complex mathematical calculations in an algorithm can be challenging.

## Types of Algorithm

Following are the types of Algorithm:

• Brute Force Algorithm
• Recursive Algorithm
• Sorting Algorithm
• Randomized Algorithm
• Searching Algorithm
• Hashing Algorithm

## Algorithm Analysis

The analysis of an algorithm helps in determining the time and space requirements for its execution. It helps in predicting the behavior of an algorithm without requiring its implementation on a specific computer.

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