The Last Algorithms Course You'll Need

• Understanding Big O Notation: Learn to evaluate algorithm efficiency using Big O notation, and understand its importance in analyzing performance.
• Data Structures: Gain proficiency in fundamental data structures, including arrays, linked lists, stacks, queues, trees, graphs, and maps, understanding their uses and operations.
• Algorithmic Techniques: Explore searching algorithms like linear and binary search, and sorting algorithms such as bubble sort and quicksort, implementing them in TypeScript.
• Problem-Solving Strategies: Develop skills to tackle complex problems like the "Two Crystal Balls Problem" using innovative and optimized approaches.
• Advanced Concepts: Delve into advanced data structures like heaps and tries, and understand graph algorithms, including breadth-first and depth-first searches, and Dijkstra's shortest path.
• Practical Applications: Implement and test algorithms using real-world scenarios and examples, preparing you for common interview questions.

This course offers a fun, beginner-friendly introduction to data structures and algorithms, utilizing TypeScript to illustrate key concepts. It is tailored for those preparing for technical interviews, providing insights into the most commonly tested topics. The course combines theoretical knowledge with practical coding exercises, ensuring a well-rounded understanding of algorithms and data structures.

• Aspiring software developers preparing for technical interviews.
• Self-taught programmers seeking to strengthen their algorithmic skills.
• Students in computer science looking to complement their academic learning.
• Professionals transitioning to roles requiring knowledge of algorithms and data structures.

• Basic understanding of programming concepts.
• Familiarity with JavaScript or TypeScript is beneficial but not mandatory.
• Willingness to engage with coding exercises and challenges.

• Introduction: Overview of course objectives and strategies for mastering algorithms.
• Basics: Explore Big O notation and foundational data structures like arrays.
• Search: Learn linear and binary search algorithms, and solve the "Two Crystal Balls Problem."
• Sort: Implement sorting techniques like bubble sort and quicksort.
• Advanced Data Structures: Delve into linked lists, trees, heaps, and graphs.
• Recursion and Pathfinding: Understand recursion and apply it to solve pathfinding problems.
• Optimization Techniques: Implement and analyze Dijkstra's shortest path and other optimization algorithms.
• Maps & LRU Cache: Explore map data structures and implement an LRU cache.