COMPLETE DSA WITH PYTHON MASTER PROGRAM

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LEVEL 1 - Module 1: Introduction to DSA

Understand data structures, algorithms, their types, and why they matter in real-world software systems.

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Learning objectives Explain what data structures are Explain what algorithms are Describe why DSA is important for developers Identify major types of data structures and algorithms Recognize real-world DSA applications

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LEVEL 1 - Module 2: Time & Space Complexity

Learn complexity fundamentals and analyze algorithms using asymptotic notations and case-based evaluation.

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Learning objectives Define time complexity Define space complexity Apply Big-O notation Differentiate Big-Omega and Big-Theta Analyze best, average, and worst cases

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LEVEL 1 - Module 3: Problem Solving Basics

Build a problem-solving framework using algorithm design steps, pseudocode, flowcharts, and dry runs.

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Learning objectives Define structured problem solving Apply algorithm design steps Write clear pseudocode Use flowcharts for visual reasoning Perform dry runs to validate logic

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LEVEL 2 - Module 4: Arrays

Learn array fundamentals, core operations, and common array-based coding patterns.

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Learning objectives Explain array structure and indexing Perform basic array operations Implement traversal, insertion, and deletion Apply searching in arrays Solve common array problems

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LEVEL 2 - Module 5: Strings

Master string concepts and operations, including manipulation, palindrome checks, and pattern matching.

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Learning objectives Define strings and their properties Use core string operations Implement common string manipulations Solve palindrome-based questions Understand basic pattern matching

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LEVEL 2 - Module 6: Two Pointer Technique

Use two-pointer and sliding-window approaches to optimize array and string problem solving.

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Learning objectives Explain the two-pointer technique Understand sliding window basics Choose pointer-based patterns for suitable problems Solve common interview-style questions

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LEVEL 3 - Module 7: Recursion

Understand recursion mechanics, base/recursive cases, recursion trees, and stack behavior.

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Learning objectives Define recursion clearly Identify base and recursive cases Visualize recursion using recursion trees Explain stack memory in recursive calls

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LEVEL 3 - Module 8: Backtracking

Solve combinatorial search problems through backtracking templates and constraints-driven exploration.

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Learning objectives Explain backtracking approach Generate subsets and permutations Model and solve N-Queens Understand Sudoku solver flow

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LEVEL 4 - Module 9: Linked List Basics

Learn linked list fundamentals, node structure, and major linked list variants.

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Learning objectives Explain linked list structure Differentiate types of linked lists Understand node representation and pointers

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LEVEL 4 - Module 10: Linked List Operations

Implement core linked list operations and solve frequent linked-list interview problems.

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Learning objectives Perform insertion and deletion Traverse and reverse linked lists Detect cycles in linked structures

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LEVEL 5 - Module 11: Stack

Understand stack behavior, implementation choices, and practical stack applications.

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Learning objectives Define stack and LIFO principle Implement stack operations Build stack using Python list Apply stacks to practical problems

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LEVEL 5 - Module 12: Queue

Learn queue variants and operations, including circular queue and deque usage.

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Learning objectives Define queue and FIFO principle Implement queue operations Differentiate circular queue and deque Use queue in real scenarios

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LEVEL 6 - Module 13: Tree Basics

Build foundational understanding of trees, tree types, and essential terminology.

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Learning objectives Define tree data structure Identify different tree types Use core tree terminology correctly

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LEVEL 6 - Module 14: Binary Trees

Learn binary tree structure and implement major traversal techniques.

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Learning objectives Define binary tree Perform inorder, preorder, and postorder traversals Implement level-order traversal

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LEVEL 6 - Module 15: Binary Search Tree (BST)

Implement BST operations and leverage ordered tree properties for efficient lookup.

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Learning objectives Define BST properties Implement BST insertion and deletion Perform efficient BST search

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LEVEL 6 - Module 16: Advanced Trees

Explore balanced trees and heaps, and understand priority-queue-driven workflows.

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Learning objectives Define balanced trees and why they matter Understand AVL tree rotations conceptually Use min-heap and max-heap concepts Apply priority queue operations

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LEVEL 7 - Module 17: Hashing

Understand hashing internals, collision strategies, and Python dictionary behavior.

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Learning objectives Explain hashing conceptually Use hash tables effectively Understand hash functions and collisions Describe Python dictionary internals at a high level

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LEVEL 8 - Module 18: Graph Basics

Learn graph structures, graph types, and common representations used in algorithms.

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Learning objectives Define graphs and graph components Differentiate major graph types Represent graphs using adjacency list and matrix

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LEVEL 8 - Module 19: Graph Traversal

Implement and compare BFS and DFS for traversal and connectivity tasks.

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Learning objectives Implement BFS traversal Implement DFS traversal Choose between BFS and DFS by problem type

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LEVEL 8 - Module 20: Graph Algorithms

Solve path, spanning, and dependency problems with core graph algorithms.

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Learning objectives Apply Dijkstra for shortest path Understand minimum spanning tree concept Perform topological sorting for DAG dependencies

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LEVEL 9 - Module 21: Searching Algorithms

Learn searching basics and implement linear and binary search with use-case awareness.

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Learning objectives Define searching in algorithmic terms Implement linear search Implement binary search with sorted data constraints

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LEVEL 9 - Module 22: Sorting Algorithms

Master foundational sorting methods and compare trade-offs across common algorithms.

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Learning objectives Define sorting and its purpose Implement bubble, selection, and insertion sort Implement merge sort and quick sort Compare sorting complexity and behavior

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LEVEL 10 - Module 23: Greedy Algorithms

Understand greedy strategy and apply it to optimization-style algorithmic problems.

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Learning objectives Define greedy approach Solve activity selection problems Analyze greedy coin change scenarios

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LEVEL 10 - Module 24: Dynamic Programming

Build DP intuition through memoization, tabulation, and classic optimization problems.

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Learning objectives Define dynamic programming and overlapping subproblems Implement memoization and tabulation Solve Fibonacci and knapsack-style problems

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LEVEL 10 - Module 25: Divide & Conquer

Learn divide-and-conquer thinking and apply it in recursive algorithm design.

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Learning objectives Define divide and conquer paradigm Understand merge sort through divide and conquer Understand quick sort partitioning strategy

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LEVEL 11 - Module 26: Problem Solving Patterns

Use interview-friendly templates to quickly identify and solve recurring problem structures.

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Learning objectives Apply sliding window pattern Apply two-pointer pattern Use prefix sums effectively Use fast and slow pointers for linked problems

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LEVEL 11 - Module 27: Advanced Problem Solving

Strengthen interview performance by solving hard problems with optimization and edge-case rigor.

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Learning objectives Practice hard-level DSA questions Apply optimization techniques to improve solutions Handle edge cases consistently and correctly