Unravel the art & science of algorithms using Python. This category offers concise tutorials, practical examples, and optimization tips for implementing data structures, sorting, searching, and complex algorithms. Perfect for beginners and pros looking to sharpen their problem-solving skills.
Merging two sorted linked lists is a classic coding problem that tests your ability to manipulate linked lists and design efficient algorithms. In this blog post, we’ll dive into the problem, explore an iterative solution in Python, provide test cases, and discuss key takeaways for acing this question in coding interviews.
Read MoreThe Two Sum problem is a staple in coding interviews, challenging you to find two numbers in an array that add up to a target sum. In this post, we’ll break down the problem, explore an efficient Python solution using a hash map, and provide comprehensive test cases to ensure correctness. Whether you're prepping for an interview or sharpening your algorithmic skills, this guide has you covered.
Read MoreThe reverse string problem is a classic coding interview question that tests your understanding of string manipulation and algorithmic thinking. In this blog post, we dive deep into the problem, explore multiple approaches to solve it, provide a robust Python solution with unit tests, and share tips to ace this question in interviews. Whether you're a beginner or brushing up for your next tech interview, this guide has you covered.
Read MoreThe sliding window algorithm is a powerful technique for solving array and string problems efficiently. By maintaining a dynamic "window" of elements, it reduces time complexity and simplifies complex tasks like finding subarrays or substrings with specific properties. In this blog post, we dive into how the algorithm works, explore its types, and walk through a practical Python example—finding the longest substring without repeating characters—complete with a solution and tests.
Read MoreBinary search is a powerful and efficient algorithm for finding elements in sorted arrays, offering logarithmic time complexity that outshines linear search for large datasets. In this post, we’ll explore how binary search works, implement it in Python with thorough tests, and analyze its time complexity to understand why it’s a cornerstone of computer science. Whether you’re a beginner or brushing up on algorithms, this guide has you covered!
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