Python DSA Syllabus
Data Structures and Algorithms (DSA) syllabus using Python
Module 1: Python Fundamentals & Problem Solving
- Concepts
- Lists, tuples, sets, dictionaries and functions
- Object-oriented programming basics
- Input/output and coding patterns
- Problem-solving approach and logical thinking
- Introduction to algorithms and data structures
Module 2: Complexity Analysis, Arrays & Strings
- Time and space complexity
- Big O, Big Ω and Big Θ notation
- Arrays and Python lists
Array traversal, insertion, deletion and searching - Two-pointer technique
- Sliding window technique
- Strings and string manipulation
Module 3: Hashing, Linked Lists & Stacks
- Hash tables and hashing concepts
- Python dictionaries and sets for efficient solutions
- Frequency counting and duplicate detection
- Singly and doubly linked lists
- Linked list insertion, deletion and traversal
- Reverse linked list
- Detecting cycles in linked lists
- Stack implementation and applications
- Monotonic stack problems
Module 4: Queues, Recursion & Backtracking
- Queue and deque concepts
- Queue implementation using Python
- Circular queue and priority queue concepts
Recursion fundamentals - Recursion tree and complexity analysis
- Backtracking concepts
- Subsets, permutations and combinations
- Maze and constraint-based problems
Module 5: Trees, BST & Heaps
- Binary trees and tree terminology
- Tree traversal: Inorder, Preorder and Postorder
- Level-order traversal
- Binary Search Trees
- BST search, insertion and deletion
- Tree height, depth and diameter
- Lowest Common Ancestor
- Heap and priority queue concepts
- Min Heap and Max
- Heap using Python
Module 6: Searching & Sorting Algorithms
- Linear search and binary search
- Binary search on sorted data
- Binary search on answer
- Search space reduction techniques
- Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort
- Sorting using Python
- Comparing sorting algorithms and complexities
Module 7: Greedy Algorithms & Advanced Problem-Solving Techniques
- Greedy algorithm concepts
- Identifying greedy problems
- Activity selection
- Fractional knapsack
Interval-based problems - Scheduling problems
- Prefix sum technique
- Difference array technique
- Two-pointer and sliding-window problem patterns
Module 8: Graphs & Graph Algorithms
- Graph representation using Python
- Adjacency list and adjacency matrix
- Directed and undirected graphs
- BFS and DFS
- Connected components
- Cycle detection
- Shortest path algorithms
- Dijkstra’s algorithm
- Topological sorting
- Minimum Spanning Tree concepts
Module 9: Dynamic Programming, Trie & Bit Manipulation
- Dynamic Programming fundamentals
- Recursion vs Memoization vs Tabulation
- Identifying DP problems
- 1D and 2D Dynamic Programming
- Common DP problem patterns
- Longest Common Subsequence
- Knapsack problems
- Introduction to Trie data structure
- Prefix and word-search problems
- Bitwise operators in Python
Module 10: Advanced DSA & Interview Preparation
- Advanced data structures and algorithms
- Union-Find / Disjoint Set
- Advanced graph problem patterns
- Advanced tree and heap problems
- Algorithm selection and optimization
- Solving problems under time constraints
- Frequently asked DSA interview questions
- Company-oriented coding problems
- Mock coding interviews
- Resume-based technical interview preparation
- Practice assignments and timed coding tests
Python DSA Trainer Profile
Our Python DSA course at Tech Concept Hub is led by a seasoned professional with 8+ years of industry experience, currently serving as a Senior Data Scientist in a leading MNC. His practical, hands-on approach ensures students gain valuable experience by working on real-world projects.
Key Highlights of our Python DSA Course
100% Practical & Hands-On Learning
Learn DSA concepts through Python implementation, coding exercises, assignments, and real problem-solving.
Interview-Focused Preparation
Practice frequently asked DSA problems and coding patterns to prepare for technical interviews and coding rounds.
Job-Oriented Problem-Solving
Master essential techniques such as Two Pointers, Sliding Window, Binary Search, Recursion, Dynamic Programming, BFS, DFS, and Greedy Algorithms.
Mock Interviews & Coding Practice
Solve timed coding challenges, company-oriented problems, and participate in mock interviews to build confidence for real job interviews.
Frequently Asked Questions
What you will learn in Data Structures and Algorithms with Python course?
This is a job-oriented Data Structures and Algorithms (DSA) with Python course designed to help you build strong problem-solving and coding skills. The training covers arrays, strings, hashing, linked lists, stacks, queues, trees, graphs, heaps, recursion, dynamic programming, sorting, searching, and advanced algorithms with practical Python implementation.
Who can join this DSA with Python course?
The course is suitable for students, freshers, graduates, working professionals, and aspiring software developers who want to improve their programming and problem-solving skills. Basic knowledge of Python programming is helpful but not mandatory if you are comfortable with programming fundamentals.
Is this DSA course suitable for beginners?
Yes. The course starts with Python fundamentals and problem-solving techniques before progressing to advanced data structures and algorithms. Concepts are explained practically with Python coding examples and hands-on problems.
Can working professionals join weekend or flexible batches?
Yes. We provide flexible batch timings, weekend classes, and fast-track courses designed for working professionals who want to upskill while managing jobs.
How is Tech Concept Hub rated by students in Pune?
We are rated 4.9/5 on Google by 900+ students, making us one of the highest-rated IT training institutes in Pune. This reflects our focus on practical training and student success.
Does Tech Concept Hub provide career counseling along with IT training?
Yes. Our trainers not only teach but also guide students in choosing the right career path. We take every effort to help our students move ahead in their career.
