Overview
Analyze and apply adaptive sliding window techniques to solve dynamic subarray and substring problems efficiently., Identify when to use fixed-size versus adaptive sliding window patterns in coding challenges., Construct algorithms using two-pointer strategies that don’t rely on traditional window structures., Solve problems involving longest or shortest subarrays that meet complex conditions (e.g. sum ≥ target, exactly K distinct values)., Use frequency maps and hash-based counters to manage dynamic constraints within a moving window., Implement in-place array and string manipulation using pointer movement and condition tracking., Debug and visualize adaptive window logic using print tracing and incremental analysis., Apply prefix sum and cumulative frequency techniques to count or optimize subarray conditions., Build reusable templates for window-shrinking problems involving constraints like duplicates or target counts., Prepare for technical interviews with confidence by mastering advanced pointer and loop control patterns.
Developers who already understand basic algorithms and want to go deeper into dynamic sliding windows., Students preparing for technical coding interviews involving subarray and substring problems., Programmers frustrated with vague explanations of sliding window patterns and looking for clear guidance., Intermediate Swift developers who want to sharpen their algorithmic thinking., Competitive programming participants seeking to master dynamic window-based problems., Self-taught developers aiming to fill gaps in their algorithm foundations., Bootcamp graduates looking for a structured approach to sliding window variations., Developers who know how to use two pointers but struggle with conditionally shrinking or expanding windows., Engineers who want to understand how adaptive logic plays out in loop design and pointer movement., Anyone who has studied fixed-size windows and wants to go beyond templates and into real-world problems.
Basic understanding of variables, loops, and conditionals in any programming language., Familiarity with Swift (or another modern language) and how functions, arrays, and dictionaries work., Comfort with writing and running simple algorithms in an IDE or code editor., Basic problem-solving experience on platforms like LeetCode, HackerRank, or CodeSignal., Understanding of arrays, strings, and hash maps as data structures., Ability to trace code using print statements or a debugger to follow variable states., Familiarity with the sliding window pattern at a basic, fixed-size level., Some exposure to two-pointer techniques, even if not fully mastered., Experience reading or implementing simple algorithms with time and space complexity in mind., Willingness to break down problems into smaller parts and iterate on a solution.
If you've ever worked with the sliding window technique and felt like the "fixed-size" window explanations were just scratching the surface, you're not alone — and you're exactly who this course is for.
Beyond Fixed Windows: Adaptive Sliding Algorithms is the first comprehensive course to focus specifically on the inner mechanics of adaptive sliding windows and pointer manipulation in iterative structures. While most resources only teach the basic fixed-size window template, this course takes you deeper — exploring how dynamic, condition-driven windows work in real coding interviews and algorithm design.
What You'll Learn
The fundamentals of adaptive (dynamic-size) sliding window problems
How to manage window boundaries based on conditions (e.g. distinct elements, sum constraints, frequency maps)
Mastery of two-pointer approaches that don’t involve explicit windowing
Real-world patterns like:
Longest/Shortest subarray that meets a condition
Substring problems with at most or exactly K distinct elements
Minimum window problems (e.g. "smallest substring containing a pattern")
Counting and optimization strategies using prefix sums and hash maps
In-place operations and performance optimization techniques
Debugging adaptive window logic through visual walkthroughs and print tracing
How interviewers expect you to adapt base patterns to new constraints
Why This Course Is Unique
This is not a survey course that glosses over templates. Instead, you'll dive deeply into how adaptive windows actually work, why they break, how to fix them, and how to apply the logic to new problems. No other course provides such an intensive breakdown of pointer-driven loop behavior in the context of algorithmic problem solving.
By the end of the course, you won’t just recognize the patterns — you'll be able to build your own adaptive solutions from scratch with full control over correctness, efficiency, and clarity.
Norbert Grover
Norbert Grover
iOS Developer
Welcome to my Udemy profile! I'm Norbert Grover, an experienced iOS Mobile Developer specializing in GraphQL implementation, with over a decade of expertise in crafting high-quality software solutions.
About Me
With a strong foundation in Swift, SwiftUI, UIKit, and Combine, I've led the integration of GraphQL into major iOS projects, achieving significant reductions in API response times and enhancing overall application performance. My commitment to Agile methodologies has consistently resulted in increased project efficiency and faster delivery times.
Professional Achievements
Achieved increase in user engagement through innovative app features and enhancements.
Enhanced app stability and user satisfaction by resolving critical bugs and improving performance.
Professional Experience
Randstad Technologies, Fidelity Investments: Designed intuitive UIs, employed Agile methodologies, and ensured scalable, testable code using MVVM and Combine.
Pragmatic Programming Content Creator: Mentored developers, established comprehensive course content, and enhanced coding proficiency in Swift and related technologies.
Klover: Maintained and implemented app features, optimized application performance, and led successful web services integration.
Akraya: Improved user experience and application performance through code optimization and REST API integration.
OPU Labs: Refactored non-maintainable codebases, integrated new features, and ensured clean, reusable code.
LOC-Eight App: Developed and optimized a parking location guidance app, enhancing user satisfaction and reducing app crash rates.
Team Gear App: Developed a youth sports organization management app, improving team efficiency and user retention.
Listen IN App: Developed a real estate MVP app, optimizing UI and image rendering, and enhancing user engagement.
Technical Skills
Languages: Objective-C, Swift
Frameworks: iOS Core OS, macOS, Cocoa, Cocoa Touch
Version Control: Git, GitHub, AWS CodeCommit
IDE: Xcode
Design Patterns: MVVM, MVC
Other: Networking & GCD, Persistence Data, Server-Side Web Development, Swift Data Structures & Algorithms, CocoaPods, Shell Scripting, Unit Testing, iOS App Store Submission, Apple SDK, Asynchronous Tasks, JSON Parsing, SwiftUI
Certifications
Project Management: Agile, Waterfall
iOS Mobile Developer Nanodegree: Udacity
I'm passionate about leveraging my expertise in iOS development and GraphQL to drive innovation and deliver high-quality, user-centric applications. Let's embark on a learning journey together and take your iOS development skills to the next level!