A comprehensive course covering data structures and algorithms from basics to advanced concepts. Master arrays, trees, graphs, complexity analysis, and modern algorithmic patterns for building efficient applications.
Production Ready
Real-world patterns and best practices
Hands-on Projects
Build real applications as you learn
Interview Ready
Prepare for technical interviews
Discover the fundamental building blocks of efficient programming. Learn what data structures are, why they matter, and how they form the foundation of all software systems.
Master the art of predicting algorithm performance. Learn time and space complexity analysis to make informed decisions about code efficiency before you write it.
Understand how computers store and access data. Learn about stack vs heap allocation, memory layout, and how data structure choice affects performance.
Master the universal language of algorithm efficiency. Learn Big O notation to communicate performance characteristics and make data-driven decisions about code optimization.
Master the foundation of most data structures. Learn array implementation, memory layout, dynamic resizing, and common operations that power countless algorithms.
Master dynamic memory allocation with linked lists. Learn pointer manipulation, implement singly and doubly linked lists, and understand when dynamic structures excel.
Unlock the magic of O(1) lookups with hash tables. Learn hash functions, collision resolution, and implement the data structure that powers databases and caches.
Master LIFO and FIFO patterns with stacks and queues. Learn the data structures that power undo operations, task scheduling, and breadth-first algorithms.
Master text processing and pattern matching algorithms. Learn efficient string manipulation, search algorithms, and the techniques that power text editors and search engines.
Understand the mathematics behind efficient algorithms. Master logarithmic thinking to recognize why certain operations scale beautifully and enable massive systems.
Master hierarchical data structures with trees. Learn binary trees, binary search trees, tree traversal algorithms, and the structures that optimize searching and sorting.
Master the ultimate data structure for modeling relationships. Learn graph representation, traversal algorithms, and shortest path finding that powers social networks and GPS systems.
Welcome to Data Structures & Algorithms Fundamentals, your comprehensive introduction to the mathematical foundations of efficient programming. Data structures and algorithms are the backbone of computer science and form the foundation for writing performant, scalable software. In this course, we start with fundamental concepts like memory management and complexity analysis, then progressively cover core data structures and advanced algorithmic patterns. You will learn essential concepts like Big O notation and array manipulation, and build up to complex topics like graph traversal and tree optimization. By the end of this course, you will understand the "essential toolkit" used by senior developers and be equipped to ace technical interviews and build production-ready applications.
This course is designed for developers from beginner to intermediate levels. No prior experience with data structures or algorithms is required. You should be comfortable with basic programming concepts: variables, functions, loops, and modern JavaScript (ES6+). If you can write simple programs and understand basic recursion, you're ready to dive into data structures.
The course is perfect for beginners who want a structured, practical introduction to computer science fundamentals, and valuable for intermediate developers preparing for technical interviews or looking to optimize their code. Ollie might say this course is like learning the "secret sauce" behind why some programs run lightning-fast while others crawl.
If you are a self-taught developer who wants to fill gaps in CS fundamentals, or you're preparing for technical interviews at top companies, this course will meet you at your skill level and help you master these essential concepts.
Unlike many theoretical computer science courses, this curriculum emphasizes practical implementation and real-world applications. We focus on understanding not just how data structures work, but when and why to use them in production code. You'll learn to analyze algorithmic complexity before writing code, implement structures from scratch in JavaScript, and see how major tech companies use these concepts in their systems. The content covers not only the structures themselves but also the analysis skills that make you a better programmer: Big O notation for performance prediction, memory management for optimization, and pattern recognition for problem-solving. We also incorporate extensive technical interview preparation. As you work through implementations and coding challenges, you'll tackle the exact problems asked at companies like Google, Facebook, and Amazon. In short, this course goes beyond academic theory to ensure you can analyze, implement, and optimize algorithms confidently in professional settings.
The course is organized into three learning tracks: Foundations, Core Structures, and Advanced Concepts. Each track contains lessons that build on the previous ones.
Throughout all tracks, content emphasizes hands-on implementation with thorough analysis. Each lesson blends theory with practical coding, and includes coding challenges that mirror real technical interview questions.
By completing this course, you will master the fundamental concepts and implementations of computer science data structures. Specifically, you will learn to:
Analyze time and space complexity using Big O notation. Understand how to predict performance before coding and make informed decisions about algorithmic trade-offs.
Understand how computers store and access data. Learn about stack vs heap allocation, memory efficiency, and how data structure choice affects performance.
Implement dynamic arrays, understand memory layout, and master common operations. Learn why arrays are the foundation of most other data structures.
Build and manipulate linked lists, understand pointer mechanics, and implement doubly-linked variations. Master dynamic memory allocation patterns.
Implement hash functions, handle collisions, and understand the magic behind O(1) lookups. Learn how hash tables power databases and caches.
Understand LIFO and FIFO patterns, implement both structures, and recognize when to apply them. Master the algorithms that power undo operations and task scheduling.
Implement efficient string processing, learn pattern matching, and understand text search algorithms used in editors and search engines.
Build binary trees, binary search trees, and understand tree traversal algorithms. Learn how trees optimize searching and enable hierarchical data organization.
Represent and traverse graphs using DFS and BFS. Implement shortest path algorithms and understand how graphs model real-world networks.
Master the mathematical concepts behind efficient algorithms. Understand why certain operations are logarithmic and how this enables massive scalability.
Identify which data structure fits specific problems. Develop the pattern recognition skills that separate senior developers from juniors.
Solve the exact coding problems asked at top tech companies. Master the communication and problem-solving skills needed to excel in technical interviews.
These topics reflect industry practice and interview requirements. As Ollie would note, mastering these concepts is like having a "superpower toolkit" that makes complex programming problems seem simple. You'll implement real systems like search engines, recommendation algorithms, and database indexing structures.
To get the most out of this course, you should be comfortable with basic programming concepts. That means knowing how to write JavaScript functions, work with variables and loops, and understand basic recursion. Familiarity with object-oriented programming concepts (classes, methods) will help when implementing complex structures. You should also have a computer set up with a code editor (like VS Code) and Node.js for running JavaScript.
You do not need any prior computer science or mathematics background. We start from first principles and build up complexity gradually. If you're not sure about some JavaScript concepts, the MDN documentation is an excellent reference. Experience with testing frameworks is helpful but not required; we'll introduce testing concepts as needed. Overall, if you can write simple programs and understand how functions work, you'll be ready for this course.
This is a comprehensive course covering fundamental computer science concepts, so it represents a significant but highly valuable time commitment. The curriculum is designed to be thorough yet accessible, with each concept building logically on previous ones. Plan on dedicating 20-25 hours total, which can be spread over several weeks of part-time study.
We recommend setting a steady pace of 2-3 hours per week. You might spend an evening on each lesson, giving yourself time to implement the data structures and work through the coding challenges. Each lesson includes practical exercises and interview-style problems—don't rush these, as implementing and debugging code is the best way to internalize these concepts. The mathematical concepts require time to sink in, so revisiting previous lessons is encouraged.
In summary, approach the course incrementally. A realistic timeline is 2-3 weeks for complete mastery, but progress will vary by background and learning style. Coding challenges and implementation projects will help you measure your progress. As you build out data structures and solve increasingly complex problems, you'll gain the confidence to tackle any algorithmic challenge. By pacing yourself steadily, you'll complete the curriculum well-prepared for technical interviews and equipped with the computer science foundation used by senior developers.
Next: Begin with Lesson 1: What Are Data Structures? →