Data Structures and Algorithms in 15 Minutes

Introduction

This tutorial provides a comprehensive overview of essential data structures and algorithms in a concise format. It aims to equip you with foundational knowledge that is crucial for coding interviews, particularly for those targeting positions at top tech companies like FAANG.

Step 1: Understanding Basic Data Structures

Familiarize yourself with the fundamental data structures, which serve as the building blocks for more complex algorithms.

• Arrays: A collection of elements identified by index or key. They are useful for storing sequences of data.
• Linked Lists: A sequence of nodes where each node contains data and a pointer to the next node. Useful for dynamic memory allocation.
• Stacks: A collection of elements that follows Last In First Out (LIFO) principle. Common operations include push (add) and pop (remove).
• Queues: A collection of elements that follows First In First Out (FIFO) principle. Operations include enqueue (add) and dequeue (remove).

Step 2: Exploring Trees and Graphs

Next, gain insight into tree and graph structures, which are vital for representing hierarchical data and relationships.

• Binary Tree: A tree where each node has at most two children. Useful for hierarchical data representation.
• Binary Search Tree (BST): A binary tree with the property that the left child is less than the parent and the right child is greater. It allows for efficient searching and sorting.
• Graphs: A collection of nodes (vertices) and edges connecting pairs of nodes. Used to represent networks.

Step 3: Implementing Search Algorithms

Learn about key searching techniques that are fundamental for manipulating data structures.

• Breadth First Search (BFS): An algorithm to traverse or search through a graph level by level.
• Depth First Search (DFS): An algorithm that explores as far as possible along a branch before backtracking.

Step 4: Sorting Techniques

Sorting algorithms are essential for organizing data efficiently.

• Merge Sort: A divide-and-conquer algorithm that divides the array into halves, sorts them, and merges them back together.
• Selection Sort: An algorithm that divides the input list into a sorted and an unsorted region, repeatedly selecting the smallest element from the unsorted region.

Step 5: Advanced Data Structures and Algorithms

Familiarize yourself with advanced concepts that are commonly used in coding interviews.

• Heaps: A special tree-based structure that satisfies the heap property (max heap or min heap). Useful for implementing priority queues.
• Hash Maps: A data structure that pairs keys to values, allowing for constant time complexity for lookups.
• Collision Handling: Techniques to resolve conflicts when two keys hash to the same index.

Step 6: Additional Algorithms to Know

Understand some additional algorithms that can help solve complex problems.

• Dijkstra's Algorithm: Used to find the shortest path between nodes in a graph.
• Topological Sort: An ordering of vertices in a directed acyclic graph, useful for scheduling tasks.

Conclusion

In this tutorial, we covered a wide array of data structures and algorithms, from basic concepts like arrays and stacks to more complex algorithms such as Dijkstra's and sorting techniques. Familiarity with these topics is essential for successful coding interviews.

Next steps include practicing coding problems that utilize these data structures and algorithms on platforms like LeetCode or HackerRank. Consider exploring the recommended resources for deeper learning, such as MIT lectures or Jomaclass.