Welcome to Sliding Window Algorithms 🪟

What is a Sliding Window?

A sliding window is a technique where a fixed-size or variable-size window moves through data (array, string, stream) to solve problems efficiently.

Array: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Window size 3:
[1, 2, 3] → [2, 3, 4] → [3, 4, 5] → ... → [8, 9, 10]

Each position processes the window without re-processing old data!

Why Sliding Window?

Naive approach: Nested loops
- Process each window from scratch
- Time: O(n * k) where k = window size
- Inefficient for large datasets

Sliding window approach:
- Remove old element, add new element
- Time: O(n)
- 100x faster!

Two Contexts

1. Algorithmic Pattern

Solve problems on arrays/strings efficiently:

• Find longest substring
• Calculate rolling averages
• Pattern matching

2. Network Protocol

TCP/IP sliding window for:

• Flow control
• Reliable delivery
• Congestion management

Real-World Applications

Algorithmic:
- Stock price moving average (finance)
- User behavior window (analytics)
- DNA sequence matching (bioinformatics)

Networking:
- TCP/IP communication
- Video streaming
- Packet loss recovery

Prerequisites

✅ Module 1: Python Basics ✅ Module 4: NumPy (for data processing)

Perfect for intermediate learners!

What You'll Learn

1. Sliding Window Fundamentals — Core pattern and intuition
2. Two-Pointer Technique — Advanced variation for matching
3. Network Sliding Window — TCP/IP protocol flow control
4. String Matching — KMP, Rabin-Karp algorithms
5. Stream Processing — Handle data streams efficiently
6. Advanced Optimization — Data structures and caching
7. Real-World Applications — Finance, analytics, systems
8. Performance Analysis — Time and space complexity

By the end, you'll solve complex problems in linear time! 🚀