Top technical interviews commonly require stating time and space complexity for solutions. Beginners often struggle to explain these complexities despite having correct algorithms. Time and space complexity measure how runtime and memory usage scale with input size, independent of machine speed. Learning to calculate complexities for simple loops, nested constructs, and recursive algorithms enables writing cleaner, faster programs and performing better in interviews. Recognizing that complexity is about growth in operations or storage with input size helps choose more efficient approaches. Practicing systematic analysis builds confidence when reporting complexity during interviews.
Imagine you appear for an interview with the top giants. You have been asked a DSA problem, and you have somehow reached a perfect solution. Now, the interviewer asks, 'Could you please let me know the time and space complexity for this code?' After gaining experience in conducting interviews, I have observed a moment of confusion among DSA beginners when answering this question. They usually struggle to explain the time and space complexity of their code solution.
If you're starting your DSA journey, these aren't just buzzwords - they are your best friends when it comes to writing efficient code. In this series, I'll walk you through how to calculate time and space complexity for any piece of code, from the simplest loops to more advanced recursive algorithms. Whether you're prepping for coding interviews or want to write cleaner, faster programs, this is the place to begin.
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