[MERGE]-master<-roadmap

Author: karthikeyankdeveloper

roadmap.md

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+# DSA Roadmap
+
+This roadmap provides a structured path for mastering Data Structures and Algorithms (DSA). Each section includes key concepts, implementation strategies, and problem-solving techniques essential for competitive programming and technical interviews.
+
+## Milestones
+
+### 1. Introduction to Data Structures and Algorithms
+- **Description**: Understand the importance of DSA in problem-solving, efficiency, and optimization. Learn about the various types of data structures and their real-world applications.
+- **Status**: Completed
+- **Issues**: NONE
+
+### 2. Arrays and Strings
+- **Description**: Learn about static data structures like arrays and strings, including their operations, limitations, and common algorithms.
+- **Status**: Completed
+- **Issues**: NONE
+
+### 3. Linked Lists
+- **Description**: Explore dynamic data structures like singly, doubly, and circular linked lists. Understand how to perform insertion, deletion, and traversal operations.
+- **Status**: Completed
+- **Issues**: NONE
+
+### 4. Stacks and Queues
+- **Description**: Study linear data structures like stacks and queues, including their implementations and use cases in algorithms like expression evaluation and breadth-first search (BFS).
+- **Status**: Completed
+- **Issues**: NONE
+
+### 5. Trees
+- **Description**: Delve into hierarchical data structures, starting with the basics of trees and moving on to various types like binary trees, binary search trees (BST), AVL trees, and more.
+- **Status**: In Progress 
+- **Issues**: NONE
+
+#### 5.1. Binary Tree Traversal
+- **Description**: Learn and implement traversal techniques for binary trees, including in-order, pre-order, and post-order traversal.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 6. Binary Search Tree (BST)
+- **Description**: Understand the properties of BSTs, how to perform search, insert, and delete operations, and solve common problems related to BSTs.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 7. Heaps and Priority Queues
+- **Description**: Study heap data structures, including min-heaps and max-heaps, and learn how to implement priority queues using heaps.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 8. Hashing
+- **Description**: Explore the concept of hashing, including hash functions, collision resolution techniques, and hash tables.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 9. Graphs
+- **Description**: Learn about graph data structures, including different representations (adjacency matrix, adjacency list), graph traversal algorithms (DFS, BFS), and shortest path algorithms.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 10. Dynamic Programming
+- **Description**: Master the technique of dynamic programming to solve complex problems by breaking them down into simpler subproblems.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 11. Sorting and Searching Algorithms
+- **Description**: Study various sorting algorithms (quick sort, merge sort, heap sort) and searching algorithms (binary search, linear search) to understand their time and space complexities.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 12. Advanced Data Structures
+- **Description**: Learn about advanced data structures like segment trees, Fenwick trees (Binary Indexed Trees), and tries, which are useful in solving specific types of problems.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 13. Greedy Algorithms
+- **Description**: Understand the greedy approach to problem-solving and how to apply it to various algorithmic challenges.
+- **Status**: In Progress
+- **Issues**: NONE
+
+### 14. Backtracking
+- **Description**: Study the backtracking technique for solving problems like N-Queens, Sudoku, and other combinatorial problems.
+- **Status**: In Progress
+- **Issues**: NONE
+
+## Conclusion
+This roadmap is designed to provide a comprehensive guide to mastering Data Structures and Algorithms. Each milestone should be completed sequentially, ensuring a strong foundation before moving on to more advanced topics. Happy learning!