Sliding Window Coding Patterns: From Novice to Ninja

Name: Sliding Window Coding Patterns: From Novice to Ninja
Rating: 5.0 (1 reviews)

Master efficient coding patterns using Swift. Solve real-world problems with fixed and dynamic sliding windows.

Created byNorbert Grover

Last updated 11/2025

English

English [Auto],

What you'll learn

Understand the fundamentals of the sliding window technique in algorithm design.
Identify scenarios where fixed-size sliding windows are the optimal solution.
Implement efficient solutions to classic sliding window problems in Swift.
Analyze the time and space complexity of sliding window algorithms.
Differentiate between fixed-size and variable-size sliding window strategies.
Apply sliding window patterns to solve real-world coding challenges like max sum, averages, and pattern detection.
Use Swift collections such as dictionaries, sets, and queues to maintain state in sliding windows.
Write reusable, testable, and performance-oriented Swift functions for window-based problems.
Debug and optimize window-based solutions using step-by-step logic and test cases.
Develop confidence to tackle interview questions that require sliding window solutions.

Course content

3 sections • 21 lectures • 2h 58m total length

What Is a Sliding Window?0:51
Sliding Window in Simple Terms0:55

So, What Does "Sliding" Really Mean?1:00
This lecture explains what the term 'sliding' actually means in the context of the sliding window functionality.
What Is a Fixed-Size Sliding Window?1:24
This lecture explains re-iterates what a fixed-size sliding window is and what the use cases are.
Sliding Window Example0:46
This lecture explains a basic sliding window.
Sliding Window Function.8:20
Average of Subarrays of Size k8:47
This lecture explains how to calculate the average from a subarray.
Explanation of the loop.1:26
This lecture just does an in-depth explanation of the loop.
Count Fixed-Size Windows with Exactly K Distinct Integers23:37
This lecture discusses the sliding window and how to use it for finding distinct values in a window. Moreover, you learn exactly what a 'distinct count' is because many coding challenges explain this in confusing ways. This lecture demonstrates that understanding a coding challenge is a skill set in an of itself.
What Does "Using State" Mean?1:07
This lecture explains what 'maintaining state' means in the context of a function or app.
Count Windows with Distinct Elements14:21
Find the longest substring with no duplicates.
Minimum Sum of Any Subarray of Size k9:27
This lecture explains how to find the minimum sum of numbers positioned in a sliding window.
Count Windows With Average Greater Than or Equal to Target9:51
This lecture is for a coding challenge that involves a sliding window.

Find the First Negative Number in Every Window of Size k19:40
This lecture covers a challenge that is a lot more difficult than it appears on the surface. You must keep track of indices of each negative number to have it represented in the appropriate window. This efficiency will be required to pass any coding interview that asks this a similar question.
The internals of a Sliding Window and Accessing the values.0:33
Demonstrating the fixed-size sliding window.
Max Value in a Window11:03
This lecture explains how to get the max value in a series of windows.
The Window Loop Explained in Detail.1:37
Count of Distinct Elements in Every Window of Size k16:39
This lecture really goes in depth with using a queue, looping for windows and dictionaries.
First Non-Repeating Number in Every Window of Size k18:16
This lecture explains the use of a queue, result and dictionary to figure out the sliding window coding challenge.
Sliding Window Maximum Frequency Element17:57
This lecture focuses on using the queue, a dictionary for mapping and creating an array for the results.
Maximum Sum of All Contiguous Windows With Unique Elements11:21
In this lecture, we explore other ways to solve without a dictionary.

Requirements

A basic understanding of programming concepts (variables, loops, conditionals).
Familiarity with Swift syntax and functions.
Ability to work with Swift arrays and dictionaries.
Understanding of control flow (if/else, for/while loops).
Some exposure to writing functions and using parameters in Swift.
Access to a Mac or a Swift-compatible coding environment (e.g. Xcode, Playgrounds).
Willingness to learn algorithmic thinking and problem-solving techniques.
Comfort reading and writing simple Swift functions in a code editor.
No prior experience with data structures or algorithms is required, but helpful.
Motivation to practice and refine logic through guided coding challenges.

Description

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.

Who this course is for:

Aspiring iOS developers preparing for technical interviews.
Swift programmers looking to strengthen their algorithm skills.
Computer science students learning common coding patterns and techniques.
Self-taught developers seeking structured problem-solving practice.
Beginner and intermediate coders who know Swift but struggle with algorithm design.
Bootcamp graduates aiming to sharpen their data structure and algorithm knowledge.
Developers transitioning into mobile development using Swift.
Competitive programming enthusiasts who want to master the sliding window technique.
Interview candidates preparing for coding rounds at tech companies.
Educators or tutors looking for ready-made examples to teach algorithmic thinking.

Sliding Window Coding Patterns: From Novice to Ninja

What you'll learn

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Course content

Introduction2 lectures • 2min

Fixed Size Sliding Window11 lectures • 1hr 20min

Using a Queue to Track Indices8 lectures • 1hr 37min

Requirements

Description

Who this course is for: