Problem Solving Learning Path
Enhance your problem-solving skills with more complex algorithms and data structures. This 12-week path builds on the foundations and prepares you for advanced algorithmic challenges.
Path Overview
Duration
12 weeks (15-20 hours/week)
Modules
6 core modules with 15 lessons
Prerequisites
Completion of Foundations path or equivalent knowledge
What You'll Learn
- Advanced data structures like trees, graphs, and heaps
- Complex algorithm design techniques
- Dynamic programming and greedy algorithms
- Backtracking and recursive problem-solving
- Optimization techniques for algorithm efficiency
Core Modules
Trees & Graphs
Master tree and graph data structures and their traversal algorithms.
Dynamic Programming
Learn to solve complex problems by breaking them down into simpler subproblems.
Greedy Algorithms
Understand how to make locally optimal choices to find global optimum solutions.
Backtracking
Explore systematic approaches to find all (or some) solutions to computational problems.
Advanced Sorting
Dive deeper into efficient sorting algorithms and their applications.
Heaps & Priority Queues
Learn about these specialized data structures and their applications in efficient algorithms.
Learning Path Structure
Each module in this learning path follows a consistent structure:
1. Theoretical Foundation
In-depth explanations of algorithms and data structures with mathematical analysis.
2. Implementation
Detailed code implementations in both Java and Python with optimization techniques.
3. Problem Sets
Medium to hard difficulty problems that apply the concepts in various contexts.
4. Case Studies
Real-world applications and problem-solving scenarios using the learned techniques.