Data Structures & Algorithms in JavaScript

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Through this course, you’ll gain a deeper understanding of advanced data structures and problem-solving techniques. You'll explore core data structures such as queues, heaps, binary search trees, and binary trees, learning how to implement and optimize them. By working through a variety of real-world coding problems, you’ll also hone your algorithmic thinking skills and ability to tackle complex coding challenges. The course is designed to walk you through the fundamentals and more advanced concepts, starting with the implementation of basic structures like queues and progressing to more intricate topics like binary search trees, heaps, and sliding window methods. You'll work with different data structures in various programming languages, including JavaScript, to optimize solutions and improve performance. As you proceed, you will also tackle problem-solving strategies using methods like sliding window, two-pointer, binary search, and dynamic programming, with ample practice problems to reinforce each technique. This course is perfect for intermediate learners familiar with basic programming concepts and data structures. If you’re looking to improve your algorithmic skills, or if you’re preparing for coding interviews, this course is the perfect way to take your understanding to the next level. By the end of the course, you will be able to implement advanced data structures, solve real-world algorithmic problems, optimize code for efficiency, and prepare for technical interviews.

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will master advanced problem-solving techniques and learn to approach algorithmic challenges with a structured mindset. By covering key techniques like dynamic programming, greedy algorithms, and graph theory, this course will equip you with the skills to solve complex problems with confidence. You'll learn how to break down large problems into smaller subproblems and optimize your solutions for efficiency and scalability. The course starts with an in-depth exploration of dynamic programming, where you will implement various problems and learn how to select the most effective strategies for solving them. Then, you'll move into greedy algorithms and their applications, followed by a deep dive into graph algorithms. The course covers graph traversal methods like BFS and DFS, as well as more complex topics like Dijkstra's algorithm, Bellman-Ford, and minimum spanning trees. Each section is accompanied by practical problems to reinforce your learning. As you progress, you'll develop critical thinking skills and a deep understanding of algorithmic principles. This course encourages hands-on problem-solving, with each problem designed to stretch your ability to approach problems from multiple angles. You'll gain practical experience in coding and problem-solving, ensuring you're well-prepared for real-world technical challenges. The course is ideal for those who already have basic programming knowledge and want to deepen their understanding of algorithms and problem-solving techniques. Whether you’re preparing for technical interviews or looking to enhance your algorithmic thinking, this course is tailored to boost your skill set. It is especially suitable for computer science enthusiasts, developers, and those aspiring to excel in technical fields. By the end of the course, you will be able to apply dynamic programming, greedy algorithms, graph traversal methods, and backtracking to solve complex problems, optimize algorithms, and enhance your problem-solving efficiency.