https://www.youtube.com/watch?v=T-P80mRXF1s

I'd like to kick things off by introducing a YouTube channel that has been instrumental in refreshing my understanding of various topics. While I may not use these patterns daily, they have undoubtedly come into play numerous times in my experiences. There's a certain satisfaction in delving deep into these concepts, exploring their intricacies, and perhaps even revisiting some theoretical definitions along the way.

In this initial dive, let's focus on algorithms and data structures—a foundational aspect of computer science that underpins so much of what we do. It's a journey that not only serves as a refresher but also reignites the excitement of discovery.

As we progress, I plan to delve into more specialized areas, particularly those related to functional programming. These patterns, nestled within the realm of functional programming, hold a special place in my heart. From elegant solutions to intricate problem-solving techniques, there's much to explore.

So, consider this just the beginning—a reminder of the fundamentals before we embark on a deeper exploration. Stay tuned for the next installment, where we'll uncover some truly fascinating solutions and delve into the world of functional programming patterns. Until then, enjoy the journey!

Data Structures

• Arrays: Fixed-size collection of elements, accessed by index. Useful for sequential access and sorting.
• Linked Lists: Collection of nodes where each node contains a data field and a reference to the next node. Good for dynamic data storage and manipulation.
• Stacks: Last-in, first-out (LIFO) collection of elements. Useful for function call stacks and undo operations.
• Queues: First-in, first-out (FIFO) collection of elements. Useful for task scheduling and breadth-first search.
• Hash Tables: Collection of key-value pairs, accessed by key. Efficient for lookup operations.
• Trees: Hierarchical data structure with a root value and subtrees of children. Useful for organizing hierarchical data and search operations.
• Graphs: Collection of nodes and edges where each edge connects two nodes. Useful for representing networks and relationships.

Algorithms

• Search Algorithms: Find an item in a data structure. Examples include linear search and binary search.
• Sort Algorithms: Arrange items in a specific order. Examples include quicksort, mergesort, and heapsort.
• Insert Algorithms: Add items to a data structure. Examples include insertion in an array or a linked list.
• Update Algorithms: Modify existing items in a data structure. Examples include updating a value in a hash table.
• Delete Algorithms: Remove items from a data structure. Examples include deleting a node in a linked list or a tree.

Examples repos

1. MakeContributions/DSA

This repository is a comprehensive collection of Data Structures and Algorithms (DSA) concepts and their implementations. It includes a wide range of problems solved in various programming languages, making it an excellent resource for both beginners and experienced developers. The repository follows a strict contribution guideline, ensuring that each problem is well-documented and organized.

2. thepranaygupta/Data-Structures-and-Algorithms

An open-source repository that contains all the Data Structures and Algorithms concepts and their implementation in several ways, including programming questions and interview questions. It aims to assist students learning Data Structures and Algorithms or preparing for interviews. This repository is particularly useful for those practicing on popular coding platforms like LeetCode, HackerRank, Codechef, and GeeksforGeeks.