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Java Data Structures & Algorithms: Visual Dsa + Leetcode
Published 11/2026
Created by Javid Bodurov
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 205 Lectures ( 22h 59m ) | Size: 9.9 GB
Master Java DSA with visual animations, Big O, trees, graphs, 39 LeetCode problems, and coding interview practice.
What you'll learn
⚡ Understand and implement arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs in Java.
⚡ Analyze time and space complexity using Big O notation and understand Java memory, references, and collections.
⚡ Implement searching, sorting, recursion, backtracking, BFS, DFS, and Dijkstra's algorithm in Java.
⚡ Apply two pointers, sliding windows, prefix sums, greedy techniques, and common coding interview patterns.
⚡ Practice 39 guided LeetCode problem walkthroughs and learn to explain solutions, edge cases, and complexity in coding interviews.
Requirements
❗ No prior knowledge of data structures or algorithms is required.
❗ Basic programming knowledge is helpful. The course includes Java environment setup and an introductory Java program.
❗ A computer capable of running Java and an IDE or code editor, such as IntelliJ IDEA or Visual Studio Code.
Description
Learn Data Structures and Algorithms in Java Through Visual Explanations, Practical Coding, and LeetCode Challenges
Master the foundations of Data Structures and Algorithms (DSA) in Java through step-by-step visual animations, practical implementations, algorithm analysis, and coding interview preparation.
This comprehensive course from VertexClass combines Java-specific programming concepts with essential computer science fundamentals. Instead of simply memorizing algorithms, you'll explore how they work internally, why they work, and when to use them.
With approximately 23 hours of video lessons and 39 guided LeetCode problem walkthroughs, you'll follow a structured learning path from foundational concepts to advanced problem-solving techniques.
Whether you're studying computer science, improving your Java programming skills, or preparing for software engineering interviews, this course will help you develop a stronger understanding of algorithms and data structures.
What Makes This Course Different?
✨Visual Explanations: Understand algorithms through detailed animations and step-by-step demonstrations.
✨Java-Focused Learning: Explore Java memory management, references, collections, and real implementation details.
✨Practical Implementations: See how important data structures and algorithms are implemented in Java.
✨39 LeetCode Walkthroughs: Practice carefully selected problems covering essential DSA topics.
✨Big O Analysis: Learn to evaluate algorithm efficiency and understand performance trade-offs.
✨Coding Interview Preparation: Develop structured problem-solving techniques and practice interview-style challenges.
✨Organized Learning Path: Progress from introductory concepts to increasingly challenging algorithms.
What You'll Learn
1. Java Fundamentals and Memory Management
Begin by setting up your Java development environment and writing your first Java program.
Explore how Java manages memory, including stack and heap allocation, primitive types, objects, references, and arrays.
Understand autoboxing, int versus Integer, == versus equals(), hashCode(), integer overflow, immutable strings, the string pool, garbage collection, and common memory-related errors.
You'll also explore Java generics and important concepts behind the Java Collections Framework.
These foundations will help you understand not only how to write Java code, but also how that code behaves internally.
2. Big O Notation and Algorithm Complexity
Learn how to evaluate the efficiency of algorithms using Big O notation.
You'll explore constant O(1), logarithmic O(log n), linear O(n), linearithmic O(n log n), quadratic O(n²), and exponential O(2^n) time complexity.
Discover how to analyze time and space complexity, compare alternative solutions, evaluate best-case and worst-case performance, and simplify complexity expressions.
These skills are essential for understanding why one solution may perform better than another.
3. Arrays, ArrayList, and Linked Lists
Explore one of the most important foundations of programming: how data is stored and accessed.
Learn array indexing, searching, insertion, deletion, dynamic resizing, amortized complexity, and how Java ArrayList works internally.
Then move into linked lists, including singly linked, doubly linked, and circular linked structures.
Implement insertion, deletion, searching, reversal, cycle detection, and slow-and-fast-pointer techniques.
You'll also compare ArrayList and LinkedList to understand their strengths, limitations, and practical use cases.
4. Stacks and Queues
Learn the LIFO and FIFO principles behind stacks and queues.
Build stack and queue implementations, explore Java's ArrayDeque and Queue interface, and understand circular queues, deques, and queues implemented using two stacks.
Apply stacks to balanced parentheses, undo and redo systems, and next greater element problems.
Use queues to explore breadth-first search, grid traversal, and shortest-path problems in unweighted grids.
5. Hash Tables, HashMap, and HashSet
Understand how hash tables provide efficient key-based lookups.
Study hash functions, collisions, separate chaining, open addressing, load factors, resizing, and performance characteristics.
Explore Java's HashMap and HashSet, including the importance of equals() and hashCode() and how Java handles heavily populated hash buckets.
Apply hashing techniques to frequency counting, grouping, and common interview problems.
6. Trees and Binary Search Trees
Discover hierarchical data structures, binary trees, and binary search trees.
Explore preorder, inorder, postorder, and level-order traversals.
Learn how to insert, search, and delete values in binary search trees while analyzing their performance.
Understand tree height, balance, recursion patterns, and Java-based tree implementations.
You'll also practice solving problems involving tree depth, traversal, and lowest common ancestors.
7. Heaps and Priority Queues
Learn how binary heaps organize data efficiently.
Explore min-heaps, max-heaps, array-based representations, sift-up and sift-down operations, and heapify.
Understand Java's PriorityQueue, custom comparators, heap sort, and top-K problem-solving techniques.
Apply these concepts to practical problems involving priority, ranking, and efficient selection.
8. Graphs and Graph Algorithms
Explore directed, undirected, and weighted graphs.
Learn how to represent graphs using adjacency lists and adjacency matrices, then implement graph structures in Java.
Study breadth-first search (BFS), depth-first search (DFS), connected components, cycle detection, topological sorting, and Dijkstra's shortest-path algorithm.
These concepts are valuable for solving connectivity, routing, dependency, and traversal problems.
9. Recursion, Memoization, and Backtracking
Build a clear understanding of recursive problem-solving.
Learn how base cases and recursive calls work, how the call stack changes during execution, and when recursion is preferable to iteration.
Explore memoization, divide and conquer, and backtracking through practical examples.
You'll also learn about recursion depth limitations and how to avoid common mistakes.
10. Searching and Sorting Algorithms
Implement and compare fundamental searching and sorting techniques.
Study linear search, binary search, lower and upper bounds, binary search on the answer, and Java's built-in binary search utilities.
Explore bubble sort, selection sort, insertion sort, merge sort, quick sort, and counting sort.
Understand the time and space complexity of each approach and examine Java's built-in sorting utilities.
11. Essential Coding Interview Patterns
Many coding interview problems are easier to recognize once you understand common algorithmic patterns.
Explore
✨ Two pointers
✨ Fast and slow pointers
✨ Fixed and variable sliding windows
✨ Prefix sums
✨ Intervals
✨ Greedy algorithms
✨ Introduction to dynamic programming
Learn how to identify these patterns and choose an appropriate strategy for solving a problem.
12. LeetCode Practice and Interview Preparation
Put your knowledge into practice through 39 guided LeetCode problem walkthroughs organized across the major course topics.
You'll explore problems involving arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, recursion, searching, sorting, and algorithmic patterns.
The course also includes a dedicated coding interview preparation section covering
✨ Understanding the technical interview process
✨ Breaking down unfamiliar coding problems
✨ Selecting appropriate data structures
✨ Explaining time and space complexity
✨ Testing edge cases
✨ Recognizing common implementation mistakes
✨ Practicing easy, medium, and hard mock interviews
The goal is to help you develop an organized and repeatable approach to technical problem-solving.
How to Get the Most Out of This Course
For the best learning experience, follow the lessons in order, especially if you're new to data structures and algorithms.
Watch the visual explanations carefully, then reproduce the Java implementations yourself.
Before viewing a LeetCode solution, try solving the problem independently. Even if you cannot complete it, attempting the problem first will make the explanation more valuable.
After each topic, review the complexity analysis and revisit any concepts that remain unclear.
Learning algorithms is not about memorizing hundreds of solutions. It's about recognizing patterns, understanding trade-offs, and building the reasoning skills needed to solve new problems.
Who Is This Course For?
This course is designed for
✨ Beginners in data structures and algorithms who want a structured introduction using Java.
✨ Java developers who want to strengthen their algorithmic problem-solving skills.
✨ Computer science and software engineering students.
✨ Aspiring software engineers preparing for technical coding interviews.
✨ LeetCode learners who prefer visual, step-by-step explanations.
✨ Programmers interested in Java collections, memory behavior, and algorithm efficiency.
Basic programming familiarity is helpful, but no previous experience with data structures and algorithms is required.
Start Building Stronger Problem-Solving Skills
By completing this course and practicing the examples, you'll develop a clearer understanding of essential data structures, important algorithms, their Java implementations, and the techniques used in technical coding interviews.
You'll be better equipped to analyze problems, compare solutions, write efficient Java code, and continue practicing increasingly challenging algorithmic questions.
Learn visually. Code confidently. Think algorithmically.
VertexClass - Learn Beyond Limits.
Who this course is for
⭐ Beginners who want to learn data structures and algorithms using Java through clear visual explanations.
⭐ Computer science and software engineering students who want to strengthen their understanding of DSA fundamentals.
⭐ Java developers who want to improve their knowledge of collections, algorithms, and problem-solving techniques.
⭐ Aspiring software engineers preparing for technical coding interviews and LeetCode challenges.
⭐ Self-taught programmers who want a structured learning path from basic data structures to advanced algorithms.
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