Performance-Driven Swift: Analyzing and Optimizing Loops
Course Description
Are you tired of failing technical interviews even though your Swift code works?
Many developers get stuck not because they can’t solve problems, but because their solutions are inefficient. In coding interviews, working code isn’t enough — you’re expected to write code that performs well and scales with input size.
This course is built for Swift developers who can write code but struggle to explain or optimize its time complexity under pressure. It teaches you how to approach coding challenges with performance in mind, from the start.
In Swift for Problem Solvers: Time Complexity and Loop Efficiency, you’ll learn:
What time complexity is, and why it matters in interviews
How to use and understand Big O notation: O(1), O(n), O(n²), O(n log n), and more
How to identify and fix inefficient loop-based solutions
How to apply techniques like sliding windows, prefix sums, and stride-based loops
How to compare solutions and reason through time vs. space trade-offs
How to benchmark Swift code to validate performance
By the end, you’ll be able to write faster, smarter Swift code — and finally stop losing points for inefficiency.
If your code works but you're still getting rejected, this course is for you.
Fix the real problem: your time complexity.
