Lesson Plan

• Describe why data compression is needed for storage and transmission.
• Distinguish between lossless and lossy compression and give real‑world examples of each.
• Explain the basic principles of common algorithms such as RLE, Huffman and LZW.
• Analyse the impact of compression on CPU usage, memory requirements and latency.
• Apply a simple compression technique to a sample data set.

• Projector or interactive whiteboard
• Slide deck summarising compression concepts
• Handout with tables of lossless/lossy examples
• Laptop with a simple compression demo (e.g., Python script)
• Worksheets for RLE and Huffman exercises
• Audio/visual samples (JPEG image, MP3 clip)

Begin with a quick poll: How many megabytes does a typical high‑resolution photo take to store? Connect this to students' experiences of limited phone storage and streaming bandwidth, then state that by the end of the lesson they will be able to explain why compression matters and identify where lossless versus lossy techniques are appropriate.

1. Do‑now (5’) – Students estimate file sizes of different media and record answers.
2. Mini‑lecture (10’) – Need for compression, storage & bandwidth constraints.
3. Interactive comparison (8’) – Lossless vs lossy table, discuss examples.
4. Algorithm showcase (12’) – Demonstrate RLE and Huffman coding with live coding.
5. Group activity (10’) – Worksheet: apply RLE to a binary string and build a Huffman tree.
6. Impact discussion (8’) – CPU, memory, latency considerations; quick think‑pair‑share.
7. Recap quiz (5’) – Kahoot or exit‑ticket with three key questions.

Summarise that compression balances resource savings with data fidelity, and that choosing the right technique depends on the application. Ask students to write one real‑world example of where lossless compression is essential as an exit ticket, and assign a short homework to research a modern compression algorithm such as Brotli.