Lab 22 · Systems and Data Lab: Data Structures in a Real Scenario (CS Tough)

🎯 Objective

Close the CS path at Tough level: move from knowing arrays, stacks and queues to defending a design choice with evidence.

🧩 Skills You Will Practice

• Arrays/lists
• stacks and queues in code
• Dictionaries for lookup
• Complexity reasoning (Big-O in plain words)

🧰 What You Need

• Python 3
• VS Code
• The Data Structures lesson
• A scenario worksheet in your notebook

📋 Step-by-Step Tasks

1. Scenario 1 — Browser history: implement back/forward behaviour with two stacks in Python. Test visiting 5 pages, going back twice, forward once, and print the current page after each move.
2. Scenario 2 — Print/task line: implement a queue using collections.deque for a helpdesk ticket line of 6 tickets. Process 3, add 2 urgent-at-the-back, and print the order — then state why a stack would be wrong here.
3. Scenario 3 — Student lookup: store 8 students in a list and in a dictionary keyed by ID. Look up the same ID in both, explain in plain words why the dictionary stays fast as data grows (near-constant lookup) while the list scan grows linearly.
4. Scenario 4 — Undo feature: add undo to a tiny text-list editor using a stack of actions. Perform 4 actions, undo 2, and show the state after each step.
5. Measure honestly: use a simple loop count or timing on a large list (for example 100,000 items) comparing list search versus dictionary lookup, and record both results in your worksheet.
6. Write the design defence: for each scenario, the structure chosen, the operation it makes fast, the operation it makes slow, and one real product that uses it.

✅ Expected Result / Deliverable

Working Python for 4 scenarios, a list-vs-dictionary measurement, and a written design defence per scenario — Tough CS thinking, with evidence.

⚠️ Common Mistakes

• Choosing a structure by familiarity instead of operations
• Claiming a timing result you did not actually run
• Forgetting the slow operation in the defence (every structure has one)