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Performance-Driven Swift: Analyzing And Optimizing Loops
Published 12/2025
Created by Norbert Grover
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 19 Lectures ( 2h 11m ) | Size: 2.31 GB [/center]
Master time complexity and loop optimization in Swift to write faster, more efficient, and scalable code.
What you'll learn
Analyze and compare the time complexity of different loop-based algorithms in Swift.
Identify performance bottlenecks in loop structures using real-world examples.
Optimize for-in, while, and stride loops for linear, logarithmic, and constant-time operations.
Convert inefficient nested loop structures into more efficient linear or sub-linear approaches.
Implement sliding window techniques to reduce time complexity from O(n²) to O(n).
Understand the role of auxiliary space in optimizing loop-based solutions.
Apply mathematical reasoning (e.g., arithmetic series, prefix sums) to reduce loop overhead.
Benchmark loop performance in Swift using native tools to validate time complexity improvements.
Differentiate between brute-force and optimized loop-based algorithms for common coding challenges.
Write clean, readable Swift code that meets both time and space efficiency goals.
Requirements
Basic knowledge of the Swift programming language (variables, functions, conditionals).
Familiarity with Swift control flow structures such as for, while, and if statements.
Understanding of array and collection operations in Swift.
Ability to write and run Swift code using Xcode or an online Swift compiler.
General understanding of what time complexity is (e.g., O(n), O(n²)) - no advanced math required.
Prior experience solving simple coding challenges or problems in Swift.
A computer with macOS and Xcode installed, or access to an online Swift playground.
Willingness to learn algorithmic thinking and improve code performance.
Comfort reading and writing basic loop structures and function definitions.
No advanced computer science background required - ideal for self-taught developers or students.
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 interviewsHow to use and understand Big O notation: O(1), O(n), O(n²), O(n log n), and moreHow to identify and fix inefficient loop-based solutionsHow to apply techniques like sliding windows, prefix sums, and stride-based loopsHow to compare solutions and reason through time vs. space trade-offsHow to benchmark Swift code to validate performanceBy 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.
Who this course is for
Swift developers who want to write more efficient and performance-aware code.
iOS development students looking to strengthen their understanding of algorithmic thinking.
Junior developers preparing for technical interviews involving time complexity.
Self-taught programmers who know Swift basics and want to deepen their problem-solving skills.
Bootcamp graduates ready to move beyond syntax and into performance tuning.
Intermediate Swift learners interested in mastering loops and performance trade-offs.
Developers transitioning from other languages to Swift, seeking algorithm fluency.
Programmers who want to refactor their Swift code for better time and space efficiency.
College students or CS learners needing practical examples of Big O concepts in Swift.
Anyone curious about how to optimize loops and patterns in Swift for real-world applications.
Performance-Driven Swift: Analyzing And Optimizing Loops
Published 12/2025
Created by Norbert Grover
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 19 Lectures ( 2h 11m ) | Size: 2.31 GB [/center]
Master time complexity and loop optimization in Swift to write faster, more efficient, and scalable code.
What you'll learn
Analyze and compare the time complexity of different loop-based algorithms in Swift.
Identify performance bottlenecks in loop structures using real-world examples.
Optimize for-in, while, and stride loops for linear, logarithmic, and constant-time operations.
Convert inefficient nested loop structures into more efficient linear or sub-linear approaches.
Implement sliding window techniques to reduce time complexity from O(n²) to O(n).
Understand the role of auxiliary space in optimizing loop-based solutions.
Apply mathematical reasoning (e.g., arithmetic series, prefix sums) to reduce loop overhead.
Benchmark loop performance in Swift using native tools to validate time complexity improvements.
Differentiate between brute-force and optimized loop-based algorithms for common coding challenges.
Write clean, readable Swift code that meets both time and space efficiency goals.
Requirements
Basic knowledge of the Swift programming language (variables, functions, conditionals).
Familiarity with Swift control flow structures such as for, while, and if statements.
Understanding of array and collection operations in Swift.
Ability to write and run Swift code using Xcode or an online Swift compiler.
General understanding of what time complexity is (e.g., O(n), O(n²)) - no advanced math required.
Prior experience solving simple coding challenges or problems in Swift.
A computer with macOS and Xcode installed, or access to an online Swift playground.
Willingness to learn algorithmic thinking and improve code performance.
Comfort reading and writing basic loop structures and function definitions.
No advanced computer science background required - ideal for self-taught developers or students.
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 interviewsHow to use and understand Big O notation: O(1), O(n), O(n²), O(n log n), and moreHow to identify and fix inefficient loop-based solutionsHow to apply techniques like sliding windows, prefix sums, and stride-based loopsHow to compare solutions and reason through time vs. space trade-offsHow to benchmark Swift code to validate performanceBy 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.
Who this course is for
Swift developers who want to write more efficient and performance-aware code.
iOS development students looking to strengthen their understanding of algorithmic thinking.
Junior developers preparing for technical interviews involving time complexity.
Self-taught programmers who know Swift basics and want to deepen their problem-solving skills.
Bootcamp graduates ready to move beyond syntax and into performance tuning.
Intermediate Swift learners interested in mastering loops and performance trade-offs.
Developers transitioning from other languages to Swift, seeking algorithm fluency.
Programmers who want to refactor their Swift code for better time and space efficiency.
College students or CS learners needing practical examples of Big O concepts in Swift.
Anyone curious about how to optimize loops and patterns in Swift for real-world applications.
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