UdaanPath
📖 Chapters
- 1. Introduction to DSA and Its Importance
- 2. Time and Space Complexity (Big O, Θ, Ω)
- 3. Recursion and Stack Memory
- 4. Mathematics for DSA (GCD, LCM, Modulo Arithmetic)
- 5. Arrays: Basics and Operations
- 6. Strings: Manipulation & Inbuilt Functions
- 7. Structures and Unions with Arrays
- 8. Linked Lists (Singly, Circular, Doubly)
- 9. Stack (Using Array & Linked List)
- 10. Queue (Simple, Circular, Deque)
- 11. Linear Search & Binary Search
- 12. Sorting Basics: Bubble, Selection, Insertion
- 13. Merge Sort
- 14. Quick Sort
- 15. Counting Sort, Radix Sort, Bucket Sort
- 16. Heap Sort
- 17. Binary Trees & Representations
- 18. Binary Tree Traversals (Pre, In, Post)
- 19. Binary Search Tree (BST)
- 20. AVL Trees (Self-balancing BST)
- 21. Trie (Prefix Tree)
- 22. Segment Tree (Range Queries)
- 23. Fenwick Tree / Binary Indexed Tree (BIT)
- 24. Heap & Priority Queues (Min-Heap, Max-Heap)
- 25. Hashing and Hash Tables
- 26. Disjoint Set Union (Union-Find, Path Compression)
- 27. Sparse Tables
- 28. Sliding Window Technique
- 29. Two Pointers Technique
- 30. Graph Representations (Adjacency Matrix & List)
- 31. BFS and DFS (Traversal & Search)
- 32. Topological Sort (Kahn’s & DFS)
- 33. Shortest Path Algorithms (Dijkstra, Bellman-Ford)
- 34. Minimum Spanning Tree (Kruskal, Prim)
- 35. Cycle Detection in Graphs
- 36. Bridges and Articulation Points
- 37. Greedy Algorithms (Activity Selection, Huffman Coding)
- 38. Backtracking (N-Queens, Sudoku Solver)
- 39. Dynamic Programming (Intro, Memoization, Tabulation)
- 40. Advanced DP (LIS, Knapsack, DP on Trees/Grids)
- 41. Bit Manipulation and XOR Tricks
- 42. Interview Problems from FAANG Companies
- 43. Competitive Coding Patterns
- 44. Building Mini Projects Using DSA (Menu-driven)
- 45. Final Quiz + Problem Sheet (100+ questions)
- 46. Next Steps: Competitive Programming or System Design?
Data Structures and Algorithms in C (DSA)
Category: IT Fundamentals & Programming
This course is designed to help learners master the core concepts of Data Structures and Algorithms (DSA) using the C programming language. Starting from the basics and advancing to complex topics like graphs, dynamic programming, and memory optimization, this course …
Introduction to DSA and Its Importance
📘 Introduction to Data Structures and Algorithms (DSA)
Data Structures and Algorithms (DSA) form the backbone of computer science and software development. Mastering DSA equips you to solve complex problems efficiently — which is why it's a top priority in interviews, projects, and real-world systems.
📦 What are Data Structures?
A Data Structure is a special format for organizing and storing data so that it can be accessed and modified effectively. The right choice of data structure can drastically improve your program's performance.
- Arrays — Simple linear collection of elements
- Linked Lists — Dynamically linked memory blocks
- Stacks — Last-In-First-Out (LIFO) operations
- Queues — First-In-First-Out (FIFO) operations
- Trees — Hierarchical data structures
- Graphs — Networks with nodes and edges
⚙️ What are Algorithms?
An Algorithm is a step-by-step procedure to solve a problem or perform a task. In programming, algorithms are paired with the right data structures to achieve optimal results.
- Sorting Algorithms — Arrange data (Bubble, Quick, Merge Sort)
- Searching Algorithms — Find data (Linear Search, Binary Search)
- Graph Algorithms — Traverse networks (BFS, DFS, Dijkstra’s)
📊 Comparison Table: Sorting Algorithms
| Algorithm | Best Case | Average Case | Worst Case | Stable? |
|---|---|---|---|---|
| Bubble Sort | O(n) | O(n²) | O(n²) | Yes |
| Quick Sort | O(n log n) | O(n log n) | O(n²) | No |
| Merge Sort | O(n log n) | O(n log n) | O(n log n) | Yes |
🧠 Analogy: Choosing the Right Tool
Just like a mechanic uses different tools for different tasks, a programmer chooses specific algorithms and data structures based on the problem at hand.
🚀 Why DSA Matters?
- 🧠 Builds logical thinking and coding discipline
- 📈 Optimizes speed and memory of programs
- 💼 Crucial for cracking interviews & coding rounds
- 📦 Helps manage large-scale systems and apps
💡 Pro Tip from UdaanPath
Don’t just memorize code — understand the problem it solves. Practice implementation and dry-run your logic. Visit UdaanPath.com for practice sets and quizzes.
✅ Summary
- DSA = Tools + Techniques to solve problems efficiently
- Every algorithm has trade-offs (speed vs memory)
- It's not about writing more code — it's about writing smart code
📤 Coming Next
In the next chapter, we’ll explore Time and Space Complexity — how to measure the performance of your programs.
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