"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."
Time complexity measures how runtime grows as input size increases; it focuses on the algorithm's growth behavior rather than machine speed. Space complexity measures the additional memory an algorithm requires beyond the input storage. Beginners should learn to count basic operations for loops, multiply costs for nested loops, and derive recurrence relations for recursive algorithms. Big O notation expresses upper bounds and simplifies growth rates by dropping constants and lower-order terms. Practicing pattern recognition for common code constructs and walking through step-by-step analyses helps accurately state time and space complexities during interviews.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]