> For the complete documentation index, see [llms.txt](https://thesigmaschool.gitbook.io/home/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://thesigmaschool.gitbook.io/home/course-curriculum/deleted-advanced-data-structures-and-algorithms.md).

# Deleted - Advanced Data Structures & Algorithms

**Course Description**

Master advanced algorithms and data structures used in competitive programming, coding interviews, and real-world software engineering. Through topics like Graphs, Trees, Dynamic Programming, and Segment Trees, you'll learn to solve complex problems with optimized and efficient solutions.

**Course Duration:** 8 Weeks

**Class Schedule:**

* 3 Classes per Week
* 1.5 Hours per Class

**Batch Timings:** Multiple batches available.

**Seat Availability:** Limited seats per batch.

**Contact:** Contact the institute for batch timings, seat availability, and enrollment details.

***

### Detailed Course Curriculum

<details>

<summary><strong>Module 1: Trees and Binary Search Trees</strong></summary>

Learn to work with tree-based data structures and solve traversal, search, and optimization problems.

Topics Included:

* Tree Traversals
* Lowest Common Ancestor (LCA)
* Tree Serialization
* Binary Search Tree Operations
* Balanced Trees

</details>

<details>

<summary><strong>Module 2: Heaps and Priority Queues</strong></summary>

Understand heap-based data structures and their applications in efficient problem-solving.

Topics Included:

* Min Heap
* Max Heap
* Priority Queue
* Heap Sort
* Top-K Problems

</details>

<details>

<summary><strong>Module 3: Graph Algorithms</strong></summary>

Master graph traversal and optimization algorithms used in competitive programming and software development.

Topics Included:

* Breadth-First Search (BFS)
* Depth-First Search (DFS)
* Shortest Path Algorithms
* Minimum Spanning Tree (MST)
* Topological Sort
* Union-Find

</details>

<details>

<summary><strong>Module 4: Greedy Algorithms</strong></summary>

Learn to solve optimization problems using greedy strategies.

Topics Included:

* Activity Selection
* Fractional Knapsack
* Job Scheduling

</details>

<details>

<summary><strong>Module 5: Backtracking</strong></summary>

Develop systematic problem-solving skills through recursive search and constraint-based algorithms.

Topics Included:

* N-Queens
* Sudoku Solver
* Rat in a Maze
* Permutations

</details>

<details>

<summary><strong>Module 6: Dynamic Programming</strong></summary>

Master one of the most important interview topics by learning to optimize recursive solutions.

Topics Included:

* Memoization
* Tabulation
* Longest Common Subsequence (LCS)
* Longest Increasing Subsequence (LIS)
* Coin Change
* Dynamic Programming on Trees

</details>

<details>

<summary><strong>Module 7: Trie</strong></summary>

Learn efficient string-based data structures for fast searching and retrieval.

Topics Included:

* Prefix Search
* Auto-complete
* Word Dictionary
* String Query Problems

</details>

<details>

<summary><strong>Module 8: Segment Trees and Binary Indexed Trees</strong></summary>

Solve range query problems using advanced tree-based data structures.

Topics Included:

* Segment Trees
* Range Queries
* Lazy Propagation
* Fenwick Tree (Binary Indexed Tree)

</details>

<details>

<summary><strong>Module 9: Disjoint Set Union (DSU)</strong></summary>

Understand Union-Find data structures for solving connectivity and graph-related problems efficiently.

Topics Included:

* Union-Find
* Path Compression
* Cycle Detection
* Kruskal's Algorithm

</details>