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JavaScript Algorithms and Data Structures Explained

A seven-part learning path on algorithms and data structures with JavaScript. Start with what algorithms are and compare linear search with binary search, then learn to measure efficiency with time complexity, space complexity, and Big O notation. Finish by implementing and comparing sorting techniques, including selection sort, merge sort, and quick sort, and by understanding how recursion, base cases, and the call stack power divide-and-conquer algorithms.

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10 questions this playlist covers — skim them for a quick takeaway, or dive into the videos above for the full picture.

This seven-part learning path covers essential computer science concepts with JavaScript: linear and binary search, algorithm complexity and Big O notation, recursion, and the selection sort, merge sort, and quick sort algorithms.

An algorithm is a step-by-step set of instructions for solving a problem or performing a task. Algorithms define how data is processed, searched, or sorted, and choosing an efficient one is key to building fast, scalable software.

Linear search checks each element one by one and works on unsorted data, with O(n) time complexity. Binary search repeatedly halves a sorted dataset to find the target, with O(log n) time complexity, which makes it much faster for large datasets.

Big O notation describes how an algorithm's performance scales as input size grows, rather than its exact runtime. Common examples are O(1) constant time, O(log n) logarithmic time, O(n) linear time, and O(n²) quadratic time.

Time complexity describes how the number of operations grows as input size increases, while space complexity describes how much memory an algorithm uses. Developers analyze both to choose the most efficient solution.

Analyze how the number of operations grows relative to the input size and focus on the most significant factor. A single loop typically results in O(n), nested loops often lead to O(n²), and approaches that halve the problem at each step result in O(log n).

Recursion is a technique where a function calls itself to solve smaller versions of the same problem. The base case is the condition that stops the calls; without it, recursion runs indefinitely and causes a stack overflow error.

Recursion solves problems by having a function call itself and relies on the call stack, while iteration repeats operations with loops such as for or while. Iteration usually uses less memory, but recursion can be clearer for hierarchical problems and divide-and-conquer algorithms.

Selection sort repeatedly finds the smallest element in the unsorted portion of an array and swaps it into position. It always performs O(n²) comparisons, even on nearly sorted data, so it is mainly used for learning and small datasets.

Merge sort divides an array into halves, sorts them recursively, and merges them back, guaranteeing O(n log n) performance and stable sorting at the cost of extra memory. Quick sort partitions data around a pivot and sorts in place, which is usually faster in practice, but it can degrade to O(n²) with poor pivot choices.

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JavaScript Algorithms and Data Structures Explained

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