In this post, we will explore various data structure and algorithm (DSA) patterns that are essential for solving coding interview problems. Understanding these patterns will help you to approach problems systematically and come up with efficient solutions.

Below is a mind map that provides a high-level overview of the different DSA patterns:

References

1. Two Pointer Problems: https://lnkd.in/dK_fB-Eg

2. Backtracking Pattern: https://lnkd.in/dDGsdfps

3. Dynamic Programming Patterns: https://lnkd.in/dX7a4cau

4. Dynamic Programming Patterns 2: https://lnkd.in/db2tAp27

5. Powerful Ultimate Binary Search Template: https://lnkd.in/dxk7kdeb

6. A general approach to backtracking questions: https://lnkd.in/drsHxsZh

7. Binary Tree Traversal & Views: https://lnkd.in/dxGcKx65

8. Graph For Beginners Problems - Pattern - Sample Solutions: https://lnkd.in/dkpyiB3R

9. A comprehensive guide and template for monotonic stack based problems: https://lnkd.in/dtmFMzDJ

10. All Types of Patterns for Bits Manipulations and How to use it: https://lnkd.in/d-rfVNx2

11. Collections of Important String questions Pattern: https://lnkd.in/dCy_j-vw

12. Leetcode Pattern 1: BFS + DFS == 25% of the problems: https://lnkd.in/dtaEpzrC

13. Topological Sort

14. 0/1 Knapsack

15. Top K elements: use Heap/priority queue

1. Two Pointers

1.1. Same Direction

1.2. Opposite Direction

1.3. Prefix Sum

1.4. Fast & Slow Pointer

1.5. Data Strutures

1.5.1. Two Pointers on Sorted Arrays

1.5.2. Two Pointers on Unsorted Arrays

1.5.3. Two Pointers with Multiple Arrays/Lists

1.5.4. Two Pointers for Searching and Filtering

1.5.5. Two Pointers for String Manipulation

1.5.6. Two Pointers for Geometric Applications

1.5.7. Two Pointers and Dynamic Programming

2. Sliding Window

2.1. Fixed-Size Sliding Window

2.2. Variable-Size Sliding Window

2.3. Sliding Window and String Manipulation

2.4. Sliding Window and Array/List Problems

2.5. Sliding Window and Graph Algorithms

2.6. Optimizing Sliding Window Solutions

3. Backtracking

3.1. Building Blocks: Recursion and Trees

3.2. Solving Constraint Satisfaction Problems

3.3. Combinatorial Problems

3.4. Pruning

3.5. Graph Traversal and Backtracking

3.6. Additional States

3.7. Optimization Problems and Backtracking

3.8. Aggregation and Memoization

3.9. Dedup

3.10. Game Solving

4. Stack

4.1. Skyline Problem

4.2. Queue with Max API

4.3. Arithmetic Expressions

5. Binary Search

5.1. Search a target in a sorted array

5.2. Search a target in a sorted array - lower_bound

5.3. Search a target in a sorted array - upper_bound

6. Tree

7. Graph

8. Dynamic Programming