📄️ Big-O Notation & Complexity Analysis
Bridging classical algorithm analysis with modern AI/ML/LLM systems: formal rigor plus practical intuition for time and space complexity.
📄️ Arrays & Strings
Indexing, slicing, and in-place operations for arrays and strings — the substrate for most other techniques.
📄️ Hash Maps & Sets
A working reference for the single most-used data structure in modern software — with the caveats that actually bite in production.
📄️ Linked Lists
Pointer-based sequential structures: mental models, in-place manipulation, and the fast/slow pointer patterns.
📄️ Recursion & The Call Stack
Theory, mechanics, visualizations, and optimization of recursion — the foundation for trees, backtracking, and DP.
📄️ Python Internals & NumPy Memory
Why Python data structures behave the way they do — object model, memory layout, and NumPy's contiguous arrays.
📄️ 🎮 Arrays & Strings (Practice)
Interactive practice for Arrays & Strings — a live in-browser Python exercise.
📄️ 🎮 Big-O (Practice)
Interactive practice for Big-O & Complexity — a live in-browser Python exercise.
📄️ 🎮 Hash Maps (Practice)
Interactive practice for Hash Maps & Sets — a live in-browser Python exercise.
📄️ 🎮 Linked Lists (Practice)
Interactive practice for Linked Lists — animated walkthrough and a live Python exercise.
📄️ 🎮 Python Internals (Practice)
Interactive practice for Python Internals & NumPy Memory — a live in-browser Python exercise.
📄️ 🎮 Recursion (Practice)
Interactive practice for Recursion & The Call Stack — a live in-browser Python exercise.