| File Name: | Sliding Window Coding Patterns: From Novice to Ninja |
| Content Source: | https://www.udemy.com/course/sliding-window-coding-patterns-from-novice-to-ninja |
| Genre / Category: | Programming |
| File Size : | 1.9 GB |
| Publisher: | Norbert Grover |
| Updated and Published: | November 12, 2025 |
Are you a Swift developer looking to level up your algorithmic skills and crack real-world coding challenges? This course is your focused guide to mastering one of the most powerful techniques in problem-solving: the fixed-size sliding window.
Sliding window algorithms are essential for solving problems that involve subarrays, sequences, or contiguous chunks of data. They’re common in technical interviews, coding competitions, and practical development scenarios — yet many developers struggle to implement them efficiently and confidently. This course changes that.
In “Sliding Window Coding Patterns: From Novice to Ninja”, you’ll learn how to:
- Identify problems that require fixed-size window solutions
- Apply efficient window logic using Swift arrays, dictionaries, and queues
- Optimize performance by maintaining state across sliding windows
- Write reusable and concise Swift functions for sliding window problems
This course includes real coding challenges that walk you through problems like:
- Finding maximum/minimum sums in a window
- Calculating averages of subarrays
- Tracking frequencies and detecting patterns
- Identifying unique or repeating values in fixed windows
Each lesson is hands-on, with step-by-step explanations and clear illustrations of how the sliding window is formed, moved, and analyzed.
If you already know basic Swift programming and want to build real algorithmic muscle — especially for interviews or personal growth — this course will help you gain clarity and confidence, one window at a time.
DOWNLOAD LINK: Sliding Window Coding Patterns: From Novice to Ninja
Sliding_Window_Coding_Patterns_From_Novice_to_Ninja.part1.rar – 1000.0 MB
Sliding_Window_Coding_Patterns_From_Novice_to_Ninja.part2.rar – 911.5 MB
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