1. Basics of Java Programming
- Java Syntax: Understand variables, data types, operators, control structures (if-else, loops).
- Object-Oriented Programming (OOP): Learn classes, objects, inheritance, polymorphism, encapsulation, and abstraction.
- Java Collections Framework (JCF): Get familiar with Java’s built-in data structures like
ArrayList, LinkedList, HashMap, HashSet, Stack, and Queue.
- Exception Handling: Learn how to handle exceptions in Java using
try, catch, finally, and custom exceptions.
- Input/Output (I/O): Understand file handling, reading from and writing to files.
- Recursion: Learn the concept of recursion and how to implement recursive methods.
2. Introduction to Data Structures
- What are Data Structures? Understand the importance and types of data structures.
- Classification: Learn about Linear (Arrays, Linked Lists, Stacks, Queues) and Non-linear (Trees, Graphs) data structures.
3. Arrays and Strings
- Array Operations: Learn basic operations like insertion, deletion, searching, and sorting.
- Multidimensional Arrays: Understand 2D arrays and their applications.
- String Manipulation: Learn common string operations using Java’s
String and StringBuilder classes.
4. Linked Lists
- Singly Linked List: Implement basic operations (insertion, deletion, traversal) using classes and objects.
- Doubly Linked List: Implement doubly linked lists and understand their advantages.
- Circular Linked List: Learn about circular linked lists and their use cases.
5. Stacks and Queues
- Stack: Learn about stack operations (push, pop, peek) and implement using arrays and linked lists.
- Queue: Understand queue operations (enqueue, dequeue) and implement using arrays and linked lists.
- Priority Queue: Learn how to implement and use a priority queue using Java’s
PriorityQueue class.
- Applications: Explore real-world applications such as expression evaluation and job scheduling.
6. Recursion
- Basic Recursion: Understand and implement recursive algorithms.
- Backtracking: Learn about backtracking techniques such as solving mazes and the N-Queens problem.
- Tail Recursion: Optimize recursive functions using tail recursion.
7. Trees
- Binary Trees: Learn the basics, traversal methods (inorder, preorder, postorder), and implementations.
- Binary Search Trees (BST): Understand BST properties and operations (insertion, deletion, search).
- AVL Trees: Learn about self-balancing trees and rotations.
- Heap: Implement binary heap and understand heap operations and heap sort.
8. Graphs
- Graph Representation: Learn adjacency matrix and adjacency list representations.
- Traversal: Implement Depth First Search (DFS) and Breadth First Search (BFS).
- Shortest Path Algorithms: Study Dijkstra’s and Bellman-Ford algorithms.
- Minimum Spanning Tree: Implement Kruskal’s and Prim’s algorithms.
9. Hashing
- Hash Functions: Learn about hash functions and their implementations in Java using
HashMap and HashSet.
- Collision Resolution: Understand collision resolution techniques like chaining and open addressing.
- Applications: Explore applications of hashing in data retrieval and storage.
10. Sorting and Searching Algorithms
- Sorting Algorithms:
- Bubble Sort, Selection Sort, Insertion Sort: Start with simple sorting algorithms.
- Merge Sort, Quick Sort, Heap Sort: Learn advanced sorting algorithms.
- Searching Algorithms:
- Linear Search: Implement linear search.
- Binary Search: Efficiently search sorted arrays using binary search.
- Searching in BST: Use binary search trees for searching.
11. Advanced Data Structures
- Trie: Learn about trie structure and its use in string matching.
- Segment Tree: Understand segment trees for range queries.
- Disjoint Set: Implement union-find algorithms for disjoint sets.
12. Algorithm Design Techniques
- Divide and Conquer: Learn this technique and apply it in algorithms like merge sort and quicksort.
- Greedy Algorithms: Understand the greedy approach with problems like Huffman coding.
- Dynamic Programming: Learn and solve dynamic programming problems such as Fibonacci and knapsack.
13. Practice and Problem-Solving
- Online Platforms: Use LeetCode, HackerRank, and Codeforces to practice DSA problems.
- Coding Competitions: Participate in coding competitions to apply DSA knowledge.
- Projects: Build projects that involve the implementation of data structures and algorithms.
14. Advanced Topics (Optional)
- Graph Algorithms: Explore advanced graph algorithms such as Floyd-Warshall and A* search.
- String Algorithms: Study string matching algorithms like KMP and Rabin-Karp.
- Complexity Analysis: Understand time and space complexity, Big-O notation, and amortized analysis.
15. Revision and Optimization
- Review Key Concepts: Revisit and revise important concepts.
- Code Optimization: Learn techniques to optimize code for efficiency and performance.
Tools and Resources:
- Books:
- "Introduction to Algorithms" by Cormen et al.
- "Data Structures and Algorithms in Java" by Robert Lafore.