Class 10 AI Chapter - Statistical Data Topic - Question/Answer - Arvindzeclass

Class 10 Artificial Intelligent Code 417 Solutions

Session 2025-26

Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Part - A, Part - B, and Python. This article provides complete solution for class 10 AI (Artificial Intelligence) Code - 417 solution according to new syllabus 2025 – 26. In this article first of all go through the AI Code - 417 syllabus and then follow chapter wise notes. Class 10 AI MCQs Quiz with answers.

Part A - Employability Skills Question/Answers

Chapter 1 -Communication Skills - II

Chapter 2 -Self - Management Skills - II

Chapter 3 - ICT Skills - II

Chapter 4 - Entrepreneurial Skills - II

Chapter 5 - Green Skills - II

Part B - Subject Specific Skills Question/Answers

Chapter 1 -AI Project Cycle & Ethics

Chapter 2 - Modelling in AI

Chapter 3 - Evaluating Models

Chapter 4 - Statistical Data

Question Answer - 1
Question Answer - 2
Question Answer - 3
Question Answer - 4

Chapter 5 - Computer Vision

Chapter 6 - Natural Language Processing

Chapter 7 - Advance Python

#What is Data Science?

Data Science is the study of collecting, analyzing, and interpreting large amounts of data to discover useful patterns, insights, and knowledge that help in decision-making.

It combines mathematics, statistics, programming, and domain knowledge to make sense of raw data.

Simple Definition

Data Science is the process of turning raw data into meaningful information and actionable insights using scientific methods and algorithms.

Key Components of Data Science

Component	Description	Example
Data Collection	Gathering raw data from different sources	Website logs, surveys, sensors
Data Cleaning	Removing errors or missing values	Fixing incomplete or duplicate data
Data Analysis	Studying the data to find patterns	Checking customer buying trends
Data Visualization	Representing data using charts or graphs	Bar graphs, heat maps, dashboards
Machine Learning	Building models that can learn and predict	Predicting house prices using past data
Communication of Results	Explaining findings in a clear way	Creating reports or dashboards for managers

Example

Imagine a shopping website wants to increase sales.

Data scientists collect data on what users click, buy, and search.
They analyze which products are most popular and what time people shop.
They create a machine learning model to recommend products.
The model helps increase customer satisfaction and sales.

Tools and Technologies Used

Programming Languages: Python, R, SQL
Libraries: Pandas, NumPy, Scikit-learn, TensorFlow
Visualization Tools: Power BI, Tableau, Matplotlib
Big Data Platforms: Hadoop, Spark

Applications of Data Science

Healthcare: Predict diseases and suggest treatments
Finance: Detect fraud and manage risk
Education: Analyze student performance
Marketing: Understand customer behavior
Transportation: Optimize delivery routes

In short:

Data Science = Data + Analysis + Machine Learning + Insight for Decisions

It’s about finding answers from data that help people and organizations make better choices.

#What are the five applications of data science?

Healthcare and Medical Diagnosis
- Use: Data science helps predict diseases, personalize treatments, and analyze medical images.
- Example: Machine learning models can detect cancer from scans earlier than traditional methods.
- Benefit: Improves patient outcomes and reduces healthcare costs.
Finance and Fraud Detection
- Use: Banks and financial institutions analyze transactions to detect fraudulent activities.
- Example: Credit card companies use predictive models to flag suspicious transactions.
- Benefit: Protects customers and prevents financial losses.
Retail and E-commerce Personalization
- Use: Data science analyzes customer behavior to offer personalized recommendations.
- Example: Netflix suggests shows, Amazon suggests products based on past behavior.
- Benefit: Enhances customer experience and boosts sales.
Predictive Maintenance in Manufacturing
- Use: Analyzing machinery sensor data to predict equipment failures before they happen.
- Example: Factories using IoT sensors detect when a machine part will fail.
- Benefit: Reduces downtime and maintenance costs.
Transportation and Logistics Optimization
- Use: Data science improves route planning, traffic management, and delivery efficiency.
- Example: Ride-sharing apps like Uber or food delivery services optimize routes in real-time.
- Benefit: Saves time, reduces fuel consumption, and increases operational efficiency.

#What is high code, low code, and no code approach?

1. High-Code Approach

Meaning: Traditional software development where professional programmers write code manually using languages like Java, Python, C++, etc.
Who uses it: Experienced software developers.
Tools: IDEs like Visual Studio, Eclipse, or PyCharm.
Advantages:
- Complete control over app design and logic.
- Can handle complex, large-scale systems.
Disadvantages:
- Time-consuming.
- Requires skilled developers.
Example: Developing a banking system or e-commerce website fully coded from scratch.

2. Low-Code Approach

Meaning: Combines visual development tools (drag-and-drop interfaces) with some manual coding.
Who uses it: Developers who want to speed up development or IT professionals with basic coding skills.
Tools: Mendix, OutSystems, Microsoft Power Apps.
Advantages:
- Faster development compared to high-code.
- Easier maintenance and deployment.
Disadvantages:
- Limited customization compared to full coding.
Example: Building an internal company app using pre-built templates but customizing a few features with code.

3. No-Code Approach

Meaning: Completely visual development — no coding required.
Who uses it: Non-programmers like business users, educators, or entrepreneurs.
Tools: Wix, Bubble, Glide, AppSheet.
Advantages:
- Very fast and easy to build apps.
- Ideal for prototyping and small-scale apps.
Disadvantages:
- Limited flexibility.
- Not suitable for highly complex applications.
Example: Creating a business website or mobile app by dragging and dropping components.

#What is No-Code AI?

No-Code AI means building and using artificial intelligence systems without writing any code.
It uses visual tools — like drag-and-drop interfaces or pre-built models — so that anyone can create, train, and deploy AI solutions easily.

Example tools:

Google Teachable Machine
Microsoft Lobe
DataRobot
Peltarion
Amazon SageMaker Canvas

So instead of coding in Python or R, users can just upload data, choose a model, and see predictions visually.

Why We Need No-Code AI

Makes AI accessible to everyone — not just programmers.
Saves time and cost — no need to build models from scratch.
Speeds up innovation — ideas can be tested quickly.
Bridges the skill gap — people in business, healthcare, or education can use AI without learning coding.
Empowers small organizations — helps startups or schools use AI tools easily.

Who Can Use It

No-Code AI is useful for non-technical users as well as technical teams:

User Type	How They Use It
Teachers / Students	Create models to recognize objects, sounds, or emotions for learning projects.
Business Analysts	Build prediction models (like sales forecasts or churn analysis).
Healthcare Workers	Detect patterns in patient data without programming.
Marketing Teams	Analyze customer behavior or sentiment automatically.
Startups	Test AI ideas quickly before hiring developers.

Advantages of No-Code AI

Easy to Use
No programming skills are needed — anyone can create AI models using visual interfaces.
Faster Development
Projects that take weeks with coding can be done in hours using drag-and-drop tools.
Cost-Effective
Reduces the need to hire data scientists or developers for basic AI tasks.
Accessibility
Makes AI available to teachers, students, startups, and small businesses.
Rapid Experimentation
Users can quickly test ideas or models without worrying about technical setup.
Integration with Other Tools
Many no-code AI platforms easily connect with Excel, Google Sheets, or cloud apps.

Disadvantages of No-Code AI

Limited Customization
You can only do what the platform allows — complex or unique models are hard to build.
Less Control
Users cannot fine-tune algorithms, data preprocessing, or training parameters.
Scalability Issues
Not suitable for large-scale enterprise systems or high-performance AI needs.
Data Privacy Concerns
Cloud-based tools might expose sensitive data if not handled properly.
Dependence on Platform
If the platform changes pricing or features, users may lose flexibility or access.
Limited Understanding of AI Concepts
Users may build models without truly understanding how AI works, leading to errors or bias.