Complexity Analysis

This lecture introduces complexity analysis, emphasizing its role as the foundation of data structures and algorithms. It discusses how different solutions to a problem can vary in performance, focusing on time and space complexity. The lecture defines good code as being both readable and scalable, with scalability involving both speed and memory efficiency. Big-O notation is introduced as a tool to evaluate and rate code performance. The lecture concludes with a promise to explore Big-O notation and complexity analysis in more depth in the next session.

Why is complexity analysis considered the backbone of coding interview problems?

• It ensures the code is bug-free.

• It helps in understanding the efficiency of different solutions.

• It guarantees the code will run on all platforms.

• It makes the code more readable.

What are the two main factors that define a good code?

• Speed and accuracy

• Readability and scalability

• Memory usage and execution time

• Syntax and semantics

What does time complexity measure?

• The speed at which an algorithm runs

• The readability of the code

• The number of lines in the code

• The amount of memory an algorithm uses

How does space complexity help in evaluating an algorithm?

• By checking the number of variables used

• By determining the extra space required

• By analyzing the code's syntax

• By measuring the time taken to execute

What is the purpose of Big-O notation?

• To simplify the code

• To debug the code

• To rate the performance of code

• To ensure code compatibility

Which of the following is NOT a rating mark used in Big-O notation?

• Good

• Horrible

• Excellent

• Average

What are the two aspects of scalability discussed in the lecture?

• Time and space

• Syntax and semantics

• Speed and accuracy

• Memory and storage