DS Tutorial

INTRODUCTION

A data structure is a specialized format for organizing, processing, retrieving, and storing data in a computer's memory. Data structures arrange data in a logical manner so that it can be used effectively, shared, and persisted. They not only store data values, but also maintain information about how those values relate to each other.

Data structures give us the possibility to manage large amounts of data efficiently for uses such as large databases and internet indexing services.

Data structures are essential ingredients in creating fast and powerful algorithms. They help in managing and organizing data, reduce complexity, and increase efficiency.

In computer science, data structures are broadly categorized into two main types: primitive and abstract.

• Primitive Data Structures: These are the fundamental, built-in data types provided directly by a programming language. Examples include integers, floating-point numbers, characters, and Booleans.
• Abstract Data Structures (ADS): These are more sophisticated data structures constructed using primitive data types. Common examples include arrays, linked lists, stacks, queues, trees, and graphs.

Let us consider an example where an employee name can be broken down into three sub-items: First, Middle, and Last. However, an ID assigned to an employee will generally be considered a single item.

Theory and Key Terminology

As we progress through this tutorial, we will encounter various theoretical concepts and technical terms essential for understanding data structures and algorithms.

• Algorithm: A step-by-step procedure or set of rules designed to solve a specific problem.
• Data Structure: A structured way of organizing and storing data.
• Time Complexity: A measure of how long an algorithm takes to execute.
• Space Complexity: A measure of memory usage by an algorithm.
• Big O Notation: Expresses an algorithm’s efficiency in worst-case scenarios.
• Recursion: A function calling itself to solve a problem.
• Divide and Conquer: Breaking a problem into smaller subproblems.
• Brute Force: A straightforward problem-solving approach.

Why Learn Data Structures?

Data Structures and Algorithms are fundamental to Computer Science. They enhance programming skills and efficiency.

Objectives of Data Structures

• Correctness: Ensures accurate operation for all valid inputs.
• Efficiency: Optimizes performance while minimizing resource usage.

Key Features of Data Structures

• Robustness: Ensures correct execution across platforms.
• Adaptability: Supports long-term software evolution.
• Reusability: Enables cost-effective software development.