Eduzan / DSA in Python
DSA Tutorial Using Python – Complete Roadmap
What are Data Structures?
Data structures are methods of organizing and storing data efficiently so it can be accessed and modified effectively.
What are Algorithms?
Algorithms are step-by-step procedures used to solve problems or perform computations efficiently.
Why Learn DSA?
- Improves problem-solving and logical thinking
- Essential for technical interviews
- Helps build efficient and scalable applications
- Forms the foundation of computer science
Why Python for DSA?
- Simple and readable syntax
- Large standard library
- Ideal for rapid prototyping and learning
- Widely used in interviews and competitive programming
Installing Python
- Installing Python on different operating systems
- Verifying installation
Choosing an IDE
- PyCharm
- Visual Studio Code
- Jupyter Notebook
Python Basics (Quick Overview)
- Variables and data types
- Loops and conditional statements
- Functions and recursion
Optional Libraries
- Using
numpyfor numerical and matrix-based algorithms
Introduction to Arrays and Lists
- Understanding Python lists as dynamic arrays
Operations
- Insertion
- Deletion
- Traversal
- Searching
Common Algorithms
- Reversing an array
- Finding maximum and minimum elements
Multi-Dimensional Arrays
- Matrices and 2D lists
- Applications of matrices
Stack Concept
- Understanding LIFO (Last In, First Out)
Stack Implementation
- Implementing stacks using Python lists
Stack Operations
- Push
- Pop
- Peek
- isEmpty
Applications
- Expression evaluation
- Backtracking
- Undo/redo mechanisms
Queue Concept
- Understanding FIFO (First In, First Out)
Queue Implementation
- Using lists
- Using
collections.deque
Queue Operations
- Enqueue
- Dequeue
- Front
- isEmpty
Types of Queues
- Circular queue
- Priority queue
Applications
- CPU scheduling
- Buffering
- Breadth-First Search (BFS)
Introduction to Linked Lists
- Singly linked lists
- Doubly linked lists
Implementation
- Creating nodes
- Linking nodes in Python
Linked Lists Operations
- Insertion
- Deletion
- Traversal
- Searching
Circular Linked Lists
- Concept and implementation
Applications
- Memory management
- Implementing stacks and queues
Tree Basics
- Terminology: root, leaf, height, depth
Binary Trees
- Types: full, complete, perfect, balanced
Implementation
- Creating binary trees in Python
Tree Traversals
- In-order
- Pre-order
- Post-order
- Level-order
Binary Search Trees (BST)
- Insertion
- Deletion
- Searching
Balanced Trees
- Introduction to AVL trees
Heap Concepts
- Min-heap
- Max-heap
Implementation
- Using lists/arrays
Heap Operations
- Insertion
- Deletion
- Heapify
Applications
- Priority queues
- Heap sort
Graph Basics
- Vertices and edges
- Weighted and unweighted graphs
Graph Representation
- Adjacency matrix
- Adjacency list
Graph Implementation
- Implementing graphs in Python
Graph Traversals
- Depth-First Search (DFS)
- Breadth-First Search (BFS)
Shortest Path Algorithms
- Dijkstra’s algorithm
- Bellman-Ford algorithm
Applications
- Networking
- Social networks
- Pathfinding
Introduction to Hashing
- Hash functions and hash tables
Implementation
- Python dictionaries
Collision Handling
- Chaining
- Open addressing
Applications
- Fast data retrieval
- Password storage
- Caching
Introduction to Sorting
- Importance of sorting
Basic Sorting Algorithms
- Bubble Sort
- Selection Sort
- Insertion Sort
Advanced Sorting Algorithms
- Merge Sort
- Quick Sort
- Heap Sort
Comparison
- Time complexity
- Space complexity
- Stability and use cases
Introduction to Searching
- Searching techniques overview
Linear Search
- Implementation and complexity
Binary Search
- Searching in sorted arrays
- Time complexity and applications
Searching in Trees and Graphs
- BFS
- DFS
Introduction to Dynamic Programming
- Overlapping subproblems
- Optimal substructure
Techniques
- Memoization
- Tabulation
Classic Problems Dynamic Programming
- Fibonacci sequence
- Knapsack problem
- Longest Common Subsequence (LCS)
- Coin change problem
Greedy Approach
- Making locally optimal choices
Classic Greedy Problems
- Activity selection
- Fractional knapsack
- Huffman coding
- Prim’s and Kruskal’s algorithms
Recursion
- Understanding recursive functions
Recursive Algorithms
- Implementing recursion in Python
Backtracking
- Concept and applications
Classic Problems of Recursion and Backtracking
- N-Queens
- Sudoku solver
- Rat in a maze
Bitwise Operations
- AND, OR, XOR, NOT, shifts
Common Techniques
- Checking odd/even
- Finding the ith bit
- Counting set bits
Applications
- Optimized algorithms
- Competitive programming
Divide and Conquer Strategy
- Breaking problems into subproblems
Classic Algorithms
- Merge Sort
- Quick Sort
- Binary Search
- Strassen’s Matrix Multiplication
Advanced Concepts
- Topological sorting
- Strongly connected components (SCC)
Shortest Path Algorithms
- Floyd-Warshall algorithm
Heuristic Algorithms
- A* search algorithm
Coding Platforms
- LeetCode
- HackerRank
- Codeforces
Interview Preparation
- Common DSA interview questions
Competitive Programming Tips
- Time complexity analysis
- Practice strategies
End of lesson.