1.What are data structures and algorithms?
2.Importance and applications in programming.

1.Analysis of algorithms (time complexity and space complexity).
2.Asymptotic notations (Big-O, Big-Theta, Big-Omega).

1.Introduction to arrays and strings.
2.Operations, advantages, and disadvantages.
3.String manipulation algorithms.

1.Singly linked lists, doubly linked lists, circular linked lists.
2.Operations (insertion, deletion, traversal).
3.Applications and advantages.

1.Concepts and operations.
2.Implementations using arrays and linked lists.
3.Applications (e.g., expression evaluation, reversing strings).

1.Binary trees, binary search trees (BST).
2.Traversal techniques (inorder, preorder, postorder).
3. AVL trees and balancing.

1.Representation (adjacency matrix, adjacency list).
2.Traversal techniques (BFS, DFS).
3.Shortest path algorithms (Dijkstra’s, Bellman-Ford).

1.Hash functions and collision handling (chaining, open addressing).
2.Hash maps and hash sets.
3. Applications and efficiency.

1.Comparison-based sorting (bubble sort, insertion sort, selection sort).
2.Efficient sorting algorithms (merge sort, quick sort, heap sort).
3.Stability and complexity analysis.

1.Concept and principles.
2.Examples (fibonacci sequence, knapsack problem).
3.Memoization and tabulation techniques.

1.Principles and characteristics.
2.Examples (minimum spanning tree, Dijkstra’s algorithm).
3.Comparison with dynamic programming.

1.Segment trees
2.Trie (prefix tree)
3.Red-black trees
4.B-trees

1.Solving problems on platforms like LeetCode, Codeforces, or HackerRank.
2.Analysis of common algorithmic problems and their solutions.