Course Overview:
This course is designed to help students master data structures and algorithms using Python. It focuses on writing clean, readable, and efficient code to solve real-world and interview-based problems. Learners will gain hands-on experience with arrays, strings, stacks, queues, linked lists, trees, graphs, and algorithm techniques like recursion, dynamic programming, and greedy algorithms.
Way of Training:
- Mode: Online / Offline
- Duration: 45 Days
- Online Sessions: 1 Hour/Day
- Offline Sessions: 1 Hour Theory + 1 Hour Lab/Practical
Data Structures and Algorithms Using Python
Course Fee:
- Online Mode: 3999/- only
- Offline Mode: 4999/- only
Includes:
- Live or recorded Python-based DSA classes
- Notebook files + code templates + dry-run logic
- Mini project using data structures
- Interview prep: coding questions + MCQs
- Telegram/WhatsApp group access for doubts
Instructor Video :
About the Course:
Python is an ideal language for learning data structures due to its simplicity and built-in data types. This course takes students from basic concepts to advanced topics, with a clear focus on logic-building, memory handling, algorithm analysis, and real-world use cases—all implemented using Python
Job Roles After Learning Python DSA:
- Python Developer with algorithmic problem-solving
- Software Engineer roles in MNCs
- Placement-ready student for coding interviews
- Backend/Data Developer with DSA foundations
- Competitive programmer with Python
Importance of Python DSA:
- Used in technical coding rounds
- Preferred for CP due to simplicity
- Base for machine learning and backend dev
- Easy syntax helps focus on logic, not syntax
- Improves clarity of recursion, loops, and memory
Course Content
Module 1: Python Basics for DSA
- Python syntax and data types overview
- List, tuple, dictionary, and set recap
- How Python handles memory
- Time and space complexity with examples
- Big O, Theta, and Omega explained using loops
Module 2: Lists (Dynamic Arrays)
- Python lists vs arrays
- Insert, delete, update, slice
- Linear search and binary search
- Mini Program: Student record manager using list
- List comprehension and sorting
Module 3: Strings and String Operations
- String declaration and indexing
- Palindrome, anagram, character count
- String slicing, reversing, pattern check
- Mini Program: Login system with validations
- String methods vs manual processing
Module 4: Sorting Algorithms
- Bubble, selection, insertion sort
- Merge sort and quick sort
- Time complexity and dry-run
- Sorting with lambda and key
- Mini Project: Sort employee records by name/salary
Module 5: Recursion
- Base case and recursive case
- Factorial, Fibonacci, sum of digits
- Backtracking basics
- Call stack visualization
- Mini Program: Subset generator using recursion
Module 6: Stack
- Stack using list
- Stack using collections.deque
- Use cases: parentheses checker, undo-redo
- Postfix expression evaluation
- Mini Project: Simple calculator with stack
Module 7: Queue and Deque
- Queue using list and deque
- Circular queue logic
- Priority Queue using heapq
- Double-ended queue operations
- Mini Task: Bank queue system
Module 8: Linked List
- Singly linked list: create, insert, delete
- Doubly linked list implementation
- Circular linked list overview
- Class-based Node structure
- Mini Program: Music playlist with next/prev navigation
Module 9: Trees
- Binary tree creation and traversal
- Binary Search Tree (BST): insert, delete, search
- Inorder, preorder, postorder
- Height, leaf node count, mirror
- Mini Task: Directory structure using tree
Module 10: Graphs
- Graph using dictionary + list
- BFS and DFS traversal
- Shortest path using Dijkstra
- Cycle detection
- Mini Project: City route planner
Module 11: Hashing
- Using dict and set for hashing
- Custom hash functions
- Handling collisions using chaining
- Mini Program: Username uniqueness checker
Module 12: Greedy Algorithms
- Activity selection
- Coin change (minimum coins)
- Fractional knapsack
- Dry-run explanation of greedy choices
- Mini Task: Job scheduling problem
Module 13: Dynamic Programming
- Memoization and tabulation
- Fibonacci using DP
- 0/1 Knapsack
- Longest Common Subsequence (LCS)
- Mini Project: Game score optimizer
Module 14: Mini Project Using Python DSA
- Project Title: Student Performance Dashboard
- Uses: List, dict, recursion, sorting
- Features: Add, delete, search, sort by marks/name
- Optional: Store data in file
- CLI Menu using while loop
Top 10 Interview Questions
- What is the difference between list and tuple in Python?
- How do you implement a stack using list?
- Explain binary search with example in Python.
- What is recursion and how does it work in Python?
- When to use dictionary vs set?
- How are trees represented in Python?
- How is BFS different from DFS?
- What is the time complexity of quick sort?
- What are greedy algorithms and when are they used?
- How do you use memoization in Python?
Why Join Techidz for Python DSA?
- Beginner-friendly Python syntax + logic building
- Hands-on coding with dry-run for every logic
- Real-world use cases and mini projects
- Proper use of inbuilt data structures
- Concept-based revision and test sheets
- Telegram/WhatsApp group for lifetime access
New & Special Features in Techidz Python DSA Course
- 100+ practice programs
- Interview-based real-time questions
- Coding practice + MCQs for every topic
- Mini project in Python using DSA
- Live support, code review, and feedback
- Lifetime access to notes, videos, and updates
