1. Programming Fundamentals:

Variables, data types, and operators
Control flow (loops, conditionals)
Functions and modular programming
Input/output and file handling

2. Basic Data Structures:

Arrays and dynamic arrays
Linked lists (singly linked, doubly linked)
Stacks and queues (array-based, linked list-based)
Hash tables (hash functions, collision handling)

3. Basic Algorithms:

Linear search
Binary search
Bubble sort
Insertion sort
Selection sort

4. Advanced Data Structures:

Trees (binary trees, binary search trees)
Balanced trees (AVL trees, red-black trees)
Heaps (binary heap, priority queue)
Graphs (representation, traversal)

5. Advanced Algorithms:

Divide and conquer (merge sort, quicksort)
Dynamic programming (Fibonacci sequence, knapsack problem)
Graph algorithms (breadth-first search, depth-first search)
Shortest path algorithms (Dijkstra’s algorithm, Bellman-Ford algorithm)

6. Problem Solving and Practice:

Solving coding problems (LeetCode, HackerRank, Codeforces)
Algorithmic complexity analysis (time complexity, space complexity)
Problem-solving techniques (logical reasoning, algorithm design)

7. Algorithmic Techniques:

Recursion and backtracking
Greedy algorithms
Sliding window technique
Two-pointer technique
Divide and conquer strategies

8. Optimization and Advanced Topics:

Algorithm analysis (asymptotic notation)
Memoization and dynamic programming optimization
Space-time tradeoffs (tradeoff between time and space complexity)
Additional data structures (heaps, hash tables, advanced graph structures)

9. Practice Projects:

Implementing data structures and algorithms in practical projects
Solving real-world problems using algorithms
Optimizing algorithms for efficiency and performance

10. Continued Learning:
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Staying updated on new algorithms and data structures
Reading books on algorithms and advanced topics
Exploring research papers and online resources for further learning


HAPPY LEARNING :) 😊


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