Introduction | DSA-30

Data structures and algorithms are fundamental concepts in computer programming.
Data structures provide a way to organize and store data efficiently, enabling easy access and manipulation.
Algorithms are step-by-step procedures for solving computational problems and utilize data structures to perform operations on data.
They enable developers to solve complex problems, improve code readability, and enhance software reliability.

Data Structure: It is a way of organizing and storing data to perform operations efficiently. Examples include arrays, linked lists, stacks, queues, trees, graphs, and hash tables.
Algorithm: It is a set of well-defined steps or instructions to solve a specific problem or perform a task.
Searching: The process of finding a particular element in a data structure.
Sorting: The process of arranging elements in a specific order, such as ascending or descending.
Insertion: Adding an element into a data structure.
Deletion: Removing an element from a data structure.
Traversal: Visiting and accessing each element in a data structure.
Recursion: A technique in which a function calls itself during its execution.

Arrays: A collection of elements stored at contiguous memory locations.
Linked Lists: A sequence of nodes where each node contains data and a reference to the next node.
Stacks: A Last-In-First-Out (LIFO) data structure that allows insertion and removal of elements from one end.
Queues: A First-In-First-Out (FIFO) data structure that allows insertion at one end and removal at the other end.
Trees: Hierarchical data structures with nodes connected by edges, commonly used for organizing hierarchical data.
Graphs: Representations of relationships between objects, consisting of vertices (nodes) connected by edges.
Hash Tables: Data structures that use a hash function to map keys to values, enabling efficient key-value pair lookups.
Searching Algorithms: Techniques to find a specific element or record within a data structure, such as linear search, binary search, or hash-based search.
Sorting Algorithms: Techniques to arrange elements in a particular order, including bubble sort, insertion sort, selection sort, merge sort, quicksort, and heapsort.

Time Complexity: A measure of the amount of time required by an algorithm to run as a function of the input size.
It helps analyze and predict the running time of an algorithm for different input sizes.
Time complexity is commonly expressed using Big O notation.
It enables comparison and evaluation of different algorithms based on their efficiency and scalability.

Big O Notation: It is a mathematical notation used to describe the upper bound or worst-case scenario of an algorithm's time complexity.
It represents the growth rate of an algorithm's running time relative to the input size.
Common Big O notations include:

By analyzing an algorithm's time complexity using Big O notation, developers can make informed decisions about choosing the most efficient algorithm for a given problem and optimize the performance of their programs.