Week 1: Foundations of Algorithmic Thinking
Begin your journey with essential algorithm concepts. This week lays the groundwork for efficient problem-solving and introduces core techniques like binary search and recursion.
Week 1 Topics
- Introduction to Algorithms
- Binary Search
- Big O Notation
- Selection Sort
- Recursion
Visualize the efficiency of binary search in sorted datasets. Step through interactive examples to understand its logarithmic time complexity and practical applications.
Key Features
- Interactive Visualization
- Step-by-Step Breakdown
- Code Explanation & Examples
Grasp the fundamentals of sorting algorithms with Selection Sort. Observe its step-by-step process and analyze its quadratic time complexity in action.
Key Features
- Animated Sorting Process
- Visual Step Tracking
- Algorithm Walkthrough
Explore the power of recursion through interactive examples. Understand how recursive functions work and visualize the call stack in action.
Key Features
- Factorial Calculation Example
- Fibonacci Sequence Visualization
- Call Stack Exploration
Visually compare different time complexities and their growth rates. Gain an intuitive understanding of Big O notation and its importance in algorithm analysis.
Key Features
- Interactive Complexity Comparison
- Visual Growth Charts
- Real-World Complexity Scenarios
Week 2: Data Structures and Recursive Strategies
Delve deeper into data structures and advanced recursion. Learn about arrays, linked lists, and divide-and-conquer algorithms, essential tools for efficient data management.
Week 2 Topics
- Arrays and Limitations
- Linked Lists (Singly & Doubly)
- Advanced Recursion
- Divide and Conquer
- Memory Management
Explore array operations and their performance characteristics. Visualize access, insertion, and deletion to understand array efficiency and limitations.
Key Features
- Visual Operation Comparison
- Performance Metrics Display
- Real-time Array Manipulation
Implement and visualize linked lists, a dynamic data structure. Compare singly and doubly linked lists and understand their advantages over arrays.
Key Features
- Interactive Node Manipulation
- Pointer Visualization
- Array vs. Linked List Comparison
Master divide and conquer algorithms using recursion. Visualize Merge Sort and understand how breaking problems down leads to efficient solutions.
Key Features
- Merge Sort Visualization
- Recursive Tree Exploration
- Step-by-Step Execution Analysis
Visualize memory allocation for different data structures. Compare contiguous and linked memory and understand memory fragmentation and usage patterns.
Key Features
- Contiguous vs. Linked Memory
- Fragmentation Demonstration
- Memory Usage Pattern Analysis
Week 3: Advanced Sorting, Hashing, and Graph Algorithms
Expand your algorithmic toolkit with advanced sorting, hashing techniques, and graph algorithms. Explore Quicksort, Hash Tables, and fundamental graph traversal methods.
Week 3 Topics
- Quicksort Algorithm
- Hash Tables & Functions
- Collision Resolution
- Graph Representation
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
Visualize Quicksort, a highly efficient sorting algorithm. Observe its partitioning process and analyze its average and worst-case time complexities.
Key Features
- Animated Pivot Selection
- Partitioning Process Visualization
- Performance Comparison
Implement and visualize Hash Tables and their key operations. Understand hash functions, collision resolution, and the efficiency of hash-based data retrieval.
Key Features
- Interactive Hash Function Demo
- Collision Resolution Visualization
- Performance Analysis
Compare different graph representations in data structures. Visualize adjacency lists and matrices and understand their trade-offs in space and time.
Key Features
- Interactive Graph Creation
- Adjacency List vs. Matrix Conversion
- Space & Time Requirement Comparison
Visualize graph traversal algorithms, Breadth-First Search (BFS) and Depth-First Search (DFS). Step through animations and understand their pathfinding strategies.
Key Features
- Step-by-Step Traversal Animation
- Path Highlighting
- Queue/Stack Visualization (BFS/DFS)
Week 4: Optimization and Proximity Algorithms
Conclude your journey with advanced algorithm paradigms. Explore Greedy Algorithms, Dynamic Programming, and the K-Nearest Neighbors algorithm for complex problem-solving.
Week 4 Topics
- Greedy Algorithms
- Dynamic Programming
- Memoization vs. Tabulation
- K-Nearest Neighbors (KNN)
- Feature Scaling & Metrics
- NP-Complete & Approximation
Visualize greedy algorithms and their decision-making processes. Understand how locally optimal choices can lead to efficient, though not always perfect, solutions.
Key Features
- Step-by-Step Decision Visualization
- Optimality vs. Sub-optimality
- Real-World Applications
Visualize Dynamic Programming solutions and subproblem structures. Master techniques like memoization and tabulation for solving complex optimization problems.
Key Features
- Subproblem Visualization
- Memoization Table Building
- Optimal Substructure Demo
Solve the classic Knapsack Problem interactively. Explore greedy and dynamic programming approaches and visualize weight-value trade-offs.
Key Features
- Interactive Item Selection
- Weight-Value Trade-off Visualization
- Greedy vs. DP Comparison
Implement the K-Nearest Neighbors algorithm for classification tasks. Visualize distance metrics and parameter tuning in this fundamental machine learning algorithm.
Key Features
- Interactive Data Classification
- Distance Metric Visualization
- Parameter Tuning Exploration