Artificial Intelligence · Class 10 · Unit 4 · Statistical Data
class 10 aipractical unit⏱️ 5 min read
No-Code AI, and Orange
This unit is different from the three before it, and it is worth knowing how before you spend time on it.
1Where this unit is examined
🔑Unit 4 carries no theory marks
In the CBSE syllabus, Unit 4 Statistical Data is given 28 practical hours, a dash for theory hours, and a dash for marks. It is assessed in the Part C practical examination, alongside Computer Vision, NLP and Advance Python — not in the written paper.
That does not make it unimportant. It makes it a different kind of important: this is a unit you are examined on by doing, at a computer, in front of an examiner.
💡What this means for revision
Time spent memorising Orange’s menus will not earn a mark in the written paper. Time spent actually building a workflow will earn marks in the practical. The one part of this unit the written paper does ask about — CSV and structured data — is the next lesson, and it is short on purpose.
2What no-code AI means
🔑No-code AI
Building and training an AI model without writing a program. Instead of typing code, you join up ready-made blocks on screen — one to load the data, one to look at it, one to train a model, one to check how well it did.
🧱
Bricks instead of a workshop
Writing code is making each part yourself from raw material. No-code is being handed a box of parts that already fit together, and deciding how to connect them. You can build a great deal that way, and you can do it on your first afternoon.
The point is that the thinking is the same. You still scope the problem, acquire data, explore it, choose a model, evaluate it and deploy it — the six stages from Unit 1. No-code changes how you express those steps, not what they are.
3Orange Data Mining
📝The tool the syllabus names
Orange Data Mining is the no-code tool CBSE names for this unit. Its purpose is to let you build a complete data-science workflow by dragging widgets onto a canvas and joining them with lines— load a file, view the table, draw a scatter plot, train a model, read the results — with no programming at all.
That is as much as is worth saying here. Orange is a tool you learn in the lab with the software open in front of you, and no description of it substitutes for that. Your practical file and your practical exam are where it counts.
💡What to carry forward instead
The transferable idea is the workflow: data goes in at one end, passes through steps that each do one job, and a result comes out at the other. That is true of Orange, of a Python script, and of every AI project you have met so far.