Get Started with COMPLETE DSA WITH PYTHON MASTER PROGRAM

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Programming

COMPLETE DSA WITH PYTHON MASTER PROGRAM

Master Data Structures and Algorithms using Python from foundational concepts to interview-ready problem solving with structured levels, practical modules, and pattern-based preparation.

  • 3 Months Duration
  • 27 Modules
  • Paid Access
Course fee ₹5000.00 ₹2500.00

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

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
Module 2 Locked

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
Module 3 Locked

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
Module 4 Locked

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
Module 5 Locked

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
Module 6 Locked

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
Module 7 Locked

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
Module 8 Locked

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
Module 9 Locked

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
Module 10 Locked

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
Module 11 Locked

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
Module 12 Locked

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
Module 13 Locked

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
Module 14 Locked

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
Module 15 Locked

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
Module 16 Locked

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
Module 17 Locked

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
Module 18 Locked

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
Module 19 Locked

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
Module 20 Locked

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
Module 21 Locked

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
Module 22 Locked

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
Module 23 Locked

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
Module 24 Locked

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
Module 25 Locked

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
Module 26 Locked

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
Module 27 Locked

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
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