LeetCode Data Structures & Algorithms Visualization – Free Course
Master LeetCode problems with visual, step-by-step solutions in Java. This comprehensive free course breaks down the most important data structures and algorithms for coding interviews, making complex concepts accessible through clear visualizations and hands-on practice.
This LeetCode Data Structures & Algorithms Visualization course is designed for developers preparing for technical interviews who want to:
- Visualize solutions step-by-step to understand how algorithms work, not just memorize code
- Master LeetCode Java solutions with clean, well-commented implementations
- Build a strong foundation in core data structures and algorithms tested in FAANG interviews
- Practice systematically through curated LeetCode problems organized by topic and difficulty
Perfect for: Bootcamp graduates, computer science students, self-taught developers, and anyone preparing for technical coding interviews at top tech companies.
This free LeetCode course covers the fundamental patterns and data structures you'll encounter in 80% of coding interviews.
| Module | Topic | Units | What You'll Learn |
|---|---|---|---|
| Module 1: Arrays and Two Pointers | Array manipulation and the two-pointer technique | 5 units | Master hash maps, in-place operations, sliding windows, and Kadane's Algorithm through problems like Two Sum (#1), Best Time to Buy and Sell Stock (#121), and Maximum Subarray (#53). |
| Module 2: Linked Lists | Pointer manipulation and linked list operations | 5 units | Understand iterative/recursive reversal, merging, and cycle detection with Fast & Slow pointers through Reverse Linked List (#206), Linked List Cycle (#141), and more. |
| Module 3: Stack and Queue | LIFO and FIFO data structures | 4 units | Learn stack-based parsing, auxiliary stacks, and monotonic stacks through Valid Parentheses (#20), Min Stack (#155), and Daily Temperatures (#739). |
| Module 4: Binary Trees | Tree traversal and recursive thinking | 4 units | Master DFS, tree manipulation, and post-order traversal through Invert Binary Tree (#226), Maximum Depth (#104), and Diameter of Binary Tree (#543). |
| Module 5: Recursion and Backtracking | Recursive problem-solving patterns | 2 units | Develop recursive thinking and backtracking through Fibonacci Number (#509) and Subsets (#78) with memoization optimization. |
Master array manipulation and the powerful two-pointer technique.
| Unit | Problem | LeetCode # | Core Concept |
|---|---|---|---|
| Unit 1 | Two Sum | #1 | Hash maps for O(n) lookups |
| Unit 2 | Best Time to Buy and Sell Stock | #121 | Single-pass optimization with running minimum |
| Unit 3 | Move Zeroes | #283 | In-place array manipulation with two pointers |
| Unit 4 | Remove Duplicates from Sorted Array | #26 | Two-pointer technique for in-place modification |
| Unit 5 | Maximum Subarray | #53 | Kadane's Algorithm for dynamic programming |
Understand pointer manipulation and linked list operations.
| Unit | Problem | LeetCode # | Core Concept |
|---|---|---|---|
| Unit 1 | Reverse Linked List | #206 | Iterative vs. recursive pointer reversal |
| Unit 2 | Merge Two Sorted Lists | #21 | Two-pointer merge technique |
| Unit 3 | Middle of the Linked List | #876 | Fast and slow pointer (Floyd's Tortoise and Hare) |
| Unit 4 | Linked List Cycle | #141 | Cycle detection with two pointers |
| Unit 5 | Remove Nth Node From End of List | #19 | Two-pointer technique with offset |
Learn LIFO and FIFO data structures for parsing and scheduling problems.
| Unit | Problem | LeetCode # | Core Concept |
|---|---|---|---|
| Unit 1 | Valid Parentheses | #20 | Stack for matching pairs |
| Unit 2 | Min Stack | #155 | Auxiliary stack for O(1) minimum tracking |
| Unit 3 | Implement Queue Using Stacks | #232 | Using two stacks to simulate FIFO behavior |
| Unit 4 | Daily Temperatures | #739 | Monotonic stack for next greater element |
Master tree traversal and recursive thinking.
| Unit | Problem | LeetCode # | Core Concept |
|---|---|---|---|
| Unit 1 | Invert Binary Tree | #226 | Recursive tree manipulation |
| Unit 2 | Maximum Depth of Binary Tree | #104 | DFS for tree height calculation |
| Unit 3 | Same Tree | #100 | Recursive tree comparison |
| Unit 4 | Diameter of Binary Tree | #543 | Post-order traversal with height tracking |
Develop the recursive mindset for complex problem-solving.
| Unit | Problem | LeetCode # | Core Concept |
|---|---|---|---|
| Unit 1 | Fibonacci Number | #509 | Basic recursion with memoization optimization |
| Unit 2 | Subsets | #78 | Backtracking for generating all combinations |
Every LeetCode problem includes step-by-step visual diagrams showing exactly how the algorithm processes data at each step. No more staring at code wondering "but how does it actually work?"
All LeetCode Java solutions follow industry best practices with clear variable names, comprehensive comments, and optimal time/space complexity. Each solution includes detailed explanations of the approach and complexity analysis.
The course is structured to build your skills systematically. Start with fundamental array problems, then progress to more complex data structures like linked lists, trees, and advanced recursion techniques.
Every problem in this LeetCode Data Structures & Algorithms course is frequently asked in technical interviews at companies like Google, Amazon, Facebook, Microsoft, and Apple. Master these 23 problems and you'll have the foundation to solve hundreds more.
| Week | Focus | Problems to Complete |
|---|---|---|
| Week 1 | Arrays and Two Pointers | Complete all 5 units in Module 1. Focus on understanding hash map optimization and the two-pointer pattern. |
| Week 2 | Linked Lists | Complete all 5 units in Module 2. Master pointer manipulation and the fast/slow pointer technique. |
| Week 3 | Stacks, Queues & Trees | Complete Modules 3 and 4. Understand LIFO/FIFO operations and tree traversal patterns. |
| Week 4 | Recursion & Review | Complete Module 5 and revisit any challenging problems. Practice explaining solutions out loud. |
-
Watch the Visualization: Start each problem by watching the step-by-step visual walkthrough to understand the algorithm before looking at code.
-
Attempt the Problem: Try solving it yourself on LeetCode. Struggling is part of learning.
-
Study the Java Solution: Review the provided solution, focusing on the approach and optimization techniques.
-
Code It Again: Close the solution and implement it from scratch to reinforce your understanding.
-
Explain It: Practice explaining the solution out loud as if you're in an interview. This builds communication skills.
Yes, this free LeetCode course is completely free. All problem visualizations, Java solutions, and explanations are accessible without any payment.
Basic Java knowledge is helpful (variables, loops, conditionals, classes), but each solution includes detailed comments explaining the code. If you're new to Java, consider reviewing Java fundamentals first. All our LeetCode Java solutions are beginner-friendly with line-by-line explanations.
Most students complete the course in 4-6 weeks with consistent daily practice (1-2 hours per day). You can go faster or slower based on your schedule and background.
That's normal and expected. Spend 30-45 minutes attempting the problem, then review the visualization and approach explanation before looking at the full solution. Learning happens through struggle.
These 23 problems cover the foundational patterns you need. After completing this course, you'll have the foundation to tackle 80% of LeetCode medium problems. For FAANG preparation, you'll want to solve 100-150 additional problems focusing on company-specific patterns.
Yes, every LeetCode Java solution in this course is interview-ready with optimal time and space complexity. Each solution includes Big O analysis and follows clean code principles that interviewers expect to see. The Java implementations are written the way you should write them during a real coding interview.
Yes, complete Java solutions with detailed explanations are provided for all 23 problems in this course. Each LeetCode Java solution includes multiple approaches when applicable (brute force, optimized), complexity analysis, and common pitfalls to avoid.
Our LeetCode Java solutions emphasize visual learning and conceptual understanding rather than just code. You'll see animated diagrams showing how data structures change at each step, making abstract algorithms concrete and memorable for interview situations.
Last Updated: January 11th, 2026