Introduction to Data Structures and Algorithms
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
Setting Up the Development Environment
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
Arrays and Lists
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
Stacks
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
Queues
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)
Linked Lists
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
Trees
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
Heaps
Heap Concepts
- Min-heap
- Max-heap
Implementation
- Using lists/arrays
Heap Operations
- Insertion
- Deletion
- Heapify
Applications
- Priority queues
- Heap sort
Graphs
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
Hashing
Introduction to Hashing
- Hash functions and hash tables
Implementation
- Python dictionaries
Collision Handling
- Chaining
- Open addressing
Applications
- Fast data retrieval
- Password storage
- Caching
Sorting Algorithms
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
Searching Algorithms
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
Dynamic Programming
Introduction to Dynamic Programming
- Overlapping subproblems
- Optimal substructure
Techniques
- Memoization
- Tabulation
Classic Problems
- Fibonacci sequence
- Knapsack problem
- Longest Common Subsequence (LCS)
- Coin change problem
Greedy Algorithms
Greedy Approach
- Making locally optimal choices
Classic Greedy Problems
- Activity selection
- Fractional knapsack
- Huffman coding
- Prim’s and Kruskal’s algorithms
Recursion and Backtracking
Recursion
- Understanding recursive functions
Recursive Algorithms
- Implementing recursion in Python
Backtracking
- Concept and applications
Classic Problems
- N-Queens
- Sudoku solver
- Rat in a maze
Bit Manipulation
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
Divide and Conquer Strategy
- Breaking problems into subproblems
Classic Algorithms
- Merge Sort
- Quick Sort
- Binary Search
- Strassen’s Matrix Multiplication
Advanced Graph Algorithms
Advanced Concepts
- Topological sorting
- Strongly connected components (SCC)
Shortest Path Algorithms
- Floyd-Warshall algorithm
Heuristic Algorithms
- A* search algorithm
Problem-Solving Practice
Coding Platforms
- LeetCode
- HackerRank
- Codeforces
Interview Preparation
- Common DSA interview questions
Competitive Programming Tips
- Time complexity analysis
- Practice strategies