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 preview of the next session, which will delve deeper into Big-O notation and complexity analysis.

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 base?

• Speed and accuracy

• Syntax and semantics

• Readability and scalability

• Memory usage and execution time

What does time complexity measure in an algorithm?

• The number of lines of code

• The readability of the code

• The speed of execution

• The amount of memory used

How does space complexity help in evaluating an algorithm?

• By evaluating the code's readability

• By measuring the speed of execution

• By determining the number of variables used

• By assessing the extra memory required

What is the purpose of Big-O notation in complexity analysis?

• To simplify the code syntax

• To ensure code compatibility

• To rate the performance of code solutions

• To debug the code

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

• Horrible

• Excellent

• Moderate

• Fair

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

• Syntax and semantics

• Time and space

• Speed and accuracy

• Memory and storage