DSA with Java

Index:

• Introduction to Data Structures and Algorithms:

• Overview of fundamental data structures (arrays, linked lists, stacks, queues).
• Importance of algorithms in computational problem-solving.
• Big-O notation and complexity analysis for evaluating algorithm efficiency.

• Arrays and Strings:

• Implementing dynamic arrays and multidimensional arrays in Java.
• String manipulation techniques and algorithms (substring search, string compression).
• Solving problems involving arrays and strings through algorithmic approaches.

• Linked Lists:

• Implementing singly linked lists, doubly linked lists, and circular linked lists.
• Operations on linked lists: insertion, deletion, traversal, and merging.
• Analyzing linked list algorithms and their applications.

• Stacks and Queues:

• Implementing stack and queue data structures using arrays and linked lists.
• Applications of stacks (expression evaluation, backtracking) and queues (BFS, job scheduling).
• Using stacks and queues in solving real-world problems.

• Trees and Binary Search Trees (BST):

• Introduction to tree data structures (binary trees, binary search trees).
• Traversal techniques: inorder, preorder, postorder traversal.
• Implementing operations on BSTs: insertion, deletion, searching.

• Graphs and Graph Algorithms:

• Representing graphs in Java (adjacency matrix, adjacency list).
• Graph traversal algorithms: breadth-first search (BFS) and depth-first search (DFS).
• Shortest path algorithms (Dijkstra’s algorithm, Bellman-Ford algorithm) and minimum spanning tree (Prim’s and Kruskal’s algorithms).

• Sorting and Searching Algorithms:

• Implementing sorting algorithms in Java (selection sort, insertion sort, merge sort, quick sort).
• Comparing sorting algorithms based on time complexity and performance.
• Searching techniques: linear search, binary search, interpolation search.

• Dynamic Programming:

• Understanding dynamic programming principles and optimization techniques.
• Solving problems with dynamic programming (fibonacci series, knapsack problem).
• Memoization and tabulation approaches for optimizing recursive algorithms.

• Advanced Data Structures:

• Hashing and hash tables: collision handling, hash functions.
• Priority queues and heaps: implementing heap data structure in Java.
• Advanced topics in data structures for specialized applications.

• Algorithm Design Techniques:

• Greedy algorithms and their applications (minimum spanning tree, Dijkstra’s algorithm).
• Divide and conquer approach: merging, sorting, and matrix multiplication.

Backtracking and recursion: solving problems with recursive backtracking