About This Guide

Welcome to the DSA with Python: Zero to Hero comprehensive learning roadmap! This guide serves as the complete reference material for our live course, organizing all topics, concepts, and learning outcomes in a structured, progressive manner.

For Course Students

If you’re enrolled in the live course, this blog is your companion resource that:

  • Mirrors the course structure - Every module taught in class is documented here
  • Serves as a reference - Quickly look up concepts, syntax, and examples
  • Tracks your progress - Follow along and check off completed modules
  • Provides continuity - Never miss a beat even if you miss a session

For Self-Learners

Even if you’re not enrolled in the course, this roadmap is designed to be:

  • Completely self-contained - Learn at your own pace with clear learning outcomes
  • Beginner-friendly - No prior programming experience required
  • Comprehensive - Covers everything from Python basics to advanced algorithms
  • Practical - Focus on real-world applications and problem-solving

What This Blog Includes

This is a living document that will be continuously updated with:

  • Detailed explanations for every topic and concept
  • Code examples with clear comments and explanations
  • Practice problems curated from LeetCode, HackerRank, and other platforms
  • Solutions and explanations for all practice problems
  • Video tutorials (where applicable)
  • Additional resources - articles, visualizations, and external references
  • Cheat sheets for quick revision
  • Common pitfalls and debugging tips
  • Interview tips and frequently asked questions

How to Use This Guide

  • Linear Learning: Start from Module 1 and progress sequentially - each module builds on previous knowledge
  • Topic Review: Use the table of contents to jump to specific topics you want to revisit
  • Practice First: Try practice problems before looking at solutions
  • Active Learning: Code along with examples rather than just reading
  • Track Progress: Check off modules as you complete them

Stay Updated

This blog is regularly updated with:

New practice problems and solutions Additional explanations based on common questions Links to helpful external resources Real-world applications and case studies Interview preparation materials

Bookmark this page and check back regularly for updates!

How to Get the Most Out of This Course

  • Practice daily - Consistency beats intensity
  • Code everything yourself - Don’t just copy-paste
  • Understand, don’t memorize - Focus on concepts, not syntax
  • Solve problems - Apply what you learn immediately
  • Ask questions - No question is too basic
  • Build projects - Apply concepts to real-world scenarios

Enroll Now

DSA with Python: Zero to Hero - Course Structure

PHASE 1: PYTHON FOUNDATIONS

Module 1: Getting Started with Python

Subtopics:

  • Introduction to programming and Python
  • Installing Python and setting up IDE
  • Writing and running first program
  • Understanding the Python interpreter

Learning Outcomes:

  • Set up Python development environment
  • Write, save, and execute basic Python programs
  • Understand how Python code is interpreted
  • Navigate basic IDE features

Module 2: Variables, Data Types & Basic Operations

Subtopics:

  • Variables and naming conventions
  • Basic data types: int, float, str, bool
  • Type conversion and type checking
  • Arithmetic operators
  • String operations and formatting
  • User input and output
  • Comments and documentation

Learning Outcomes:

  • Declare and use variables following Python conventions
  • Work with all basic data types confidently
  • Perform type conversions when needed
  • Create formatted string outputs
  • Build simple interactive programs

Module 3: Control Flow - Decision Making

Subtopics:

  • Boolean logic and comparison operators
  • if, elif, else statements
  • Nested conditionals
  • Logical operators (and, or, not)
  • Truthy and falsy values
  • Ternary operators

Learning Outcomes:

  • Write programs that make decisions based on conditions
  • Use complex boolean expressions effectively
  • Implement multi-way decision structures
  • Apply logical operators to combine conditions

Module 4: Control Flow - Loops

Subtopics:

  • while loops
  • for loops and range()
  • Nested loops
  • break, continue, pass statements
  • Loop control and infinite loops
  • Iterating over sequences

Learning Outcomes:

  • Choose appropriate loop structures
  • Write and control loops with precision
  • Avoid and debug infinite loops
  • Implement nested loop patterns
  • Solve repetitive tasks programmatically

Module 5: Functions

Subtopics:

  • Defining and calling functions
  • Parameters and arguments (positional, keyword, default)
  • Return values and multiple returns
  • Variable scope (local, global, nonlocal)
  • Docstrings
  • Lambda functions
  • *args and **kwargs
  • Recursion basics

Learning Outcomes:

  • Design and implement reusable functions
  • Use different parameter types effectively
  • Understand and apply variable scope rules
  • Write well-documented functions
  • Solve problems using recursion

Module 6: Lists

Subtopics:

  • Creating and accessing lists
  • List methods (append, insert, remove, pop, etc.)
  • List slicing and indexing
  • List comprehensions
  • Nested lists and 2D lists
  • Common list operations

Learning Outcomes:

  • Create and manipulate lists effectively
  • Use list comprehensions for elegant code
  • Work with multi-dimensional lists
  • Choose appropriate list methods

Module 7: Tuples & Sets

Subtopics:

  • Tuples: creation, immutability, use cases
  • Tuple packing and unpacking
  • Sets: creation and characteristics
  • Set operations (union, intersection, difference)
  • Frozen sets
  • When to use tuples vs lists vs sets

Learning Outcomes:

  • Use tuples for immutable data storage
  • Implement set operations for unique collections
  • Choose the right data structure for each use case
  • Apply set theory operations in programming

Module 8: Dictionaries

Subtopics:

  • Creating and accessing dictionaries
  • Dictionary methods
  • Iterating over dictionaries
  • Nested dictionaries
  • Dictionary comprehensions
  • defaultdict and Counter

Learning Outcomes:

  • Model real-world data using dictionaries
  • Perform efficient lookups and updates
  • Work with complex nested structures
  • Use dictionary comprehensions effectively

Module 9: Strings Deep Dive

Subtopics:

  • String methods and operations
  • String slicing and indexing
  • String formatting (f-strings, format(), %)
  • Regular expressions basics
  • String immutability
  • Common string patterns

Learning Outcomes:

  • Manipulate strings using various methods
  • Use advanced formatting techniques
  • Apply basic regular expressions
  • Solve string-based problems efficiently

Module 10: File Handling

Subtopics:

  • Reading and writing text files
  • File modes and context managers
  • Working with CSV files
  • JSON file handling
  • Exception handling with files
  • File paths and directories

Learning Outcomes:

  • Read from and write to files safely
  • Handle different file formats
  • Implement proper error handling for file operations
  • Manage file resources using context managers

Module 11: Exception Handling

Subtopics:

  • Understanding exceptions and errors
  • try, except, else, finally blocks
  • Raising exceptions
  • Custom exceptions
  • Best practices for error handling
  • Debugging techniques

Learning Outcomes:

  • Write robust code that handles errors gracefully
  • Create and raise custom exceptions
  • Debug programs effectively
  • Implement defensive programming practices

Module 12: Object-Oriented Programming - Part 1

Subtopics:

  • Classes and objects
  • init method
  • Instance vs class attributes
  • Methods
  • Encapsulation and private attributes
  • Property decorators
  • str and repr

Learning Outcomes:

  • Design and implement classes
  • Create objects and use them effectively
  • Apply encapsulation principles
  • Write clean, object-oriented code

Module 13: Object-Oriented Programming - Part 2

Subtopics:

  • Inheritance and super()
  • Method overriding
  • Polymorphism
  • Abstract classes
  • Multiple inheritance
  • Composition vs inheritance
  • Magic/dunder methods

Learning Outcomes:

  • Implement inheritance hierarchies
  • Use polymorphism effectively
  • Choose between inheritance and composition
  • Leverage Python’s special methods

Module 14: Python Standard Library & Tools

Subtopics:

  • Modules and packages
  • math, random, datetime, collections
  • Generator expressions
  • itertools and functools
  • map, filter, reduce
  • Installing packages with pip

Learning Outcomes:

  • Navigate and use the standard library
  • Write efficient code using built-in tools
  • Install and manage external packages
  • Apply functional programming tools

PHASE 2: ALGORITHM FOUNDATIONS

Module 15: Introduction to Algorithms & Complexity

Subtopics:

  • What are algorithms?
  • Algorithm characteristics
  • Big O notation (O(1), O(n), O(n²), O(log n), O(n log n))
  • Best, average, worst case analysis
  • Space complexity
  • Analyzing code complexity

Learning Outcomes:

  • Understand what makes a good algorithm
  • Analyze time and space complexity
  • Use Big O notation correctly
  • Compare algorithms based on efficiency

Module 16: Searching Algorithms

Subtopics:

  • Linear search
  • Binary search
  • Jump search
  • Interpolation search
  • When to use each algorithm

Learning Outcomes:

  • Implement various searching algorithms
  • Choose the appropriate search algorithm
  • Understand trade-offs between algorithms
  • Apply searching techniques to real problems

Module 17: Sorting Algorithms - Part 1

Subtopics:

  • Bubble sort
  • Selection sort
  • Insertion sort
  • Comparison and analysis
  • Stability in sorting

Learning Outcomes:

  • Implement basic sorting algorithms
  • Understand sorting stability
  • Analyze sorting algorithm efficiency
  • Recognize when to use simple sorts

Module 18: Sorting Algorithms - Part 2

Subtopics:

  • Merge sort
  • Quick sort
  • Heap sort
  • Counting sort
  • Python’s built-in sort (Timsort)

Learning Outcomes:

  • Implement advanced sorting algorithms
  • Understand divide-and-conquer strategy
  • Apply efficient sorting in practical scenarios
  • Use Python’s sorting effectively

Module 19: Recursion Deep Dive

Subtopics:

  • Understanding recursion thoroughly
  • Base cases and recursive cases
  • Stack overflow and limits
  • Tail recursion
  • Recursion vs iteration
  • Backtracking introduction

Learning Outcomes:

  • Write recursive solutions confidently
  • Identify problems suitable for recursion
  • Convert between recursive and iterative solutions
  • Debug recursive code

PHASE 3: DATA STRUCTURES

Module 20: Arrays and Dynamic Arrays

Subtopics:

  • Array fundamentals
  • Static vs dynamic arrays
  • Python lists as dynamic arrays
  • Array operations and complexity
  • Multi-dimensional arrays

Learning Outcomes:

  • Understand array internals and behavior
  • Work efficiently with arrays
  • Implement array-based solutions
  • Optimize array operations

Module 21: Linked Lists - Part 1

Subtopics:

  • Singly linked lists
  • Node structure and implementation
  • Insertion operations
  • Deletion operations
  • Traversal and searching
  • Linked list vs array

Learning Outcomes:

  • Implement singly linked lists from scratch
  • Perform all basic operations efficiently
  • Understand pointer/reference manipulation
  • Choose between arrays and linked lists

Module 22: Linked Lists - Part 2

Subtopics:

  • Doubly linked lists
  • Circular linked lists
  • Common linked list problems
  • Floyd’s cycle detection
  • Reversing linked lists

Learning Outcomes:

  • Implement different linked list variants
  • Solve intermediate linked list problems
  • Apply two-pointer techniques
  • Handle edge cases properly

Module 23: Stacks

Subtopics:

  • Stack ADT and LIFO principle
  • Stack implementation (array and linked list)
  • Stack operations: push, pop, peek
  • Applications: balanced parentheses, expression evaluation
  • Infix, prefix, postfix conversions

Learning Outcomes:

  • Implement stacks using different methods
  • Solve stack-based problems
  • Apply stacks to real-world scenarios
  • Understand recursion through stacks

Module 24: Queues

Subtopics:

  • Queue ADT and FIFO principle
  • Queue implementation (array and linked list)
  • Circular queues
  • Deque (double-ended queue)
  • Priority queues (introduction)

Learning Outcomes:

  • Implement various queue types
  • Use queues for problem-solving
  • Understand queue applications
  • Apply appropriate queue structures

Module 25: Hash Tables

Subtopics:

  • Hashing concept and hash functions
  • Collision resolution (chaining, open addressing)
  • Hash table implementation
  • Load factor and rehashing
  • Python dictionaries and sets internals

Learning Outcomes:

  • Understand hashing thoroughly
  • Implement hash tables from scratch
  • Design good hash functions
  • Solve problems using hash tables

Module 26: Trees - Part 1 (Binary Trees)

Subtopics:

  • Tree terminology and concepts
  • Binary tree structure and implementation
  • Tree traversals (inorder, preorder, postorder, level-order)
  • Recursive vs iterative traversals
  • Tree height, depth, and size

Learning Outcomes:

  • Implement binary trees
  • Perform all tree traversals
  • Calculate tree properties
  • Solve basic tree problems

Module 27: Trees - Part 2 (Binary Search Trees)

Subtopics:

  • BST properties and implementation
  • BST operations (insert, search, delete)
  • BST traversal applications
  • Balanced vs unbalanced BSTs
  • Time complexity analysis

Learning Outcomes:

  • Implement fully functional BSTs
  • Perform efficient BST operations
  • Understand BST advantages and limitations
  • Solve BST-specific problems

Module 28: Trees - Part 3 (Advanced Trees)

Subtopics:

  • AVL trees (self-balancing)
  • Red-Black trees (overview)
  • B-trees (concept)
  • Tries (prefix trees)
  • Segment trees (introduction)

Learning Outcomes:

  • Understand self-balancing concepts
  • Implement tries
  • Know when to use advanced tree structures
  • Appreciate real-world tree applications

Module 29: Heaps and Priority Queues

Subtopics:

  • Heap property (min-heap, max-heap)
  • Heap implementation using arrays
  • Heapify operations
  • Heap sort
  • Priority queue implementation
  • Python’s heapq module

Learning Outcomes:

  • Implement heaps from scratch
  • Use heaps for priority queues
  • Apply heaps to solve problems
  • Use Python’s heap module effectively

Module 30: Graphs - Part 1 (Fundamentals)

Subtopics:

  • Graph terminology
  • Graph representations (adjacency matrix, list)
  • Graph implementation
  • Graph traversals (BFS, DFS)
  • Connected components
  • Cycle detection

Learning Outcomes:

  • Implement graphs using different representations
  • Perform graph traversals
  • Detect graph properties
  • Choose appropriate representations

Module 31: Graphs - Part 2 (Advanced Algorithms)

Subtopics:

  • Shortest path (Dijkstra’s, Bellman-Ford)
  • Minimum spanning trees (Prim’s, Kruskal’s)
  • Topological sorting
  • Strongly connected components
  • Union-Find data structure

Learning Outcomes:

  • Implement classic graph algorithms
  • Solve shortest path problems
  • Find minimum spanning trees
  • Apply graphs to real-world problems

PHASE 4: ADVANCED ALGORITHMS

Module 32: Greedy Algorithms

Subtopics:

  • Greedy algorithm paradigm
  • Activity selection problem
  • Huffman coding
  • Fractional knapsack
  • Job sequencing
  • When greedy works and limitations

Learning Outcomes:

  • Identify greedy algorithm opportunities
  • Implement greedy solutions
  • Prove greedy correctness
  • Recognize greedy limitations

Module 33: Dynamic Programming - Part 1

Subtopics:

  • DP fundamentals
  • Memoization vs tabulation
  • Fibonacci and basic problems
  • 0/1 Knapsack problem
  • Longest common subsequence
  • Coin change problem

Learning Outcomes:

  • Understand DP principles thoroughly
  • Implement memoization and tabulation
  • Solve classic DP problems
  • Recognize DP problem patterns

Module 34: Dynamic Programming - Part 2

Subtopics:

  • Matrix chain multiplication
  • Edit distance
  • Longest increasing subsequence
  • Partition problems
  • DP on trees and graphs
  • Space optimization techniques

Learning Outcomes:

  • Solve advanced DP problems
  • Optimize DP solutions
  • Apply DP to complex scenarios
  • Develop DP intuition

Module 35: Backtracking

Subtopics:

  • Backtracking concept and template
  • N-Queens problem
  • Sudoku solver
  • Subset sum
  • Permutations and combinations
  • Constraint satisfaction problems

Learning Outcomes:

  • Implement backtracking solutions
  • Optimize backtracking with pruning
  • Solve combinatorial problems
  • Recognize backtracking opportunities

Module 36: Divide and Conquer

Subtopics:

  • Divide and conquer paradigm
  • Recurrence relations
  • Master theorem
  • Binary search analysis
  • Merge and quick sort analysis
  • Closest pair of points

Learning Outcomes:

  • Apply divide and conquer strategy
  • Analyze divide and conquer complexity
  • Solve problems using this paradigm
  • Understand when to divide and conquer

Module 37: String Algorithms

Subtopics:

  • Pattern matching (KMP, Rabin-Karp)
  • Longest common substring
  • String hashing
  • Anagram and palindrome problems
  • Edit distance applications
  • Suffix arrays (introduction)

Learning Outcomes:

  • Implement efficient string algorithms
  • Solve string manipulation problems
  • Apply pattern matching techniques
  • Handle string problems optimally

Module 38: Bit Manipulation

Subtopics:

  • Binary representation
  • Bitwise operators
  • Common bit tricks
  • Counting set bits
  • Power of two problems
  • Bit masking techniques
  • XOR properties

Learning Outcomes:

  • Use bitwise operations effectively
  • Solve problems using bit manipulation
  • Optimize solutions with bit tricks
  • Understand binary arithmetic

PHASE 5: PROBLEM-SOLVING & PRACTICE

Module 39: Problem-Solving Strategies

Subtopics:

  • How to approach DSA problems
  • Pattern recognition
  • Breaking down complex problems
  • Debugging strategies
  • Writing clean, efficient code
  • Test case design

Learning Outcomes:

  • Develop systematic problem-solving approach
  • Recognize common problem patterns
  • Debug code efficiently
  • Write production-quality solutions

Module 40: Competitive Programming Essentials

Subtopics:

  • Fast I/O techniques
  • Common templates and snippets
  • Time and space optimization
  • Online judge platforms
  • Contest strategies
  • Practice techniques

Learning Outcomes:

  • Solve problems under time constraints
  • Optimize code for competitions
  • Use online platforms effectively
  • Build competitive programming skills

Module 42: Capstone Projects

Duration: 3-4 weeks

Projects:

  • Custom data structure library
  • Text editor with undo/redo
  • Task scheduler application
  • Route optimization system
  • Spell checker with tries
  • LRU cache implementation

Learning Outcomes:

  • Apply all learned concepts to real projects
  • Make architectural decisions
  • Write production-ready code
  • Document and present technical work