Class 9 Artificial Intelligence Code 417 Solutions
Session 2025 - 26
Artificial Intelligence class 9 code 417 NCERT Section - A and Section B solutions PDF and Question Answer are provided and class 9 AI book code 417 solutions. Class 9 AI Syllabus. Class 9 AI Book. First go through Artificial Intelligence (code 417 class 9 with solutions) and then solve MCQs and Sample Papers, information technology code 417 class 9 solutions pdf is the vocational subject.
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Chapter - Data Literacy
Other Topics
Data Literacy Process Framework
The Data Literacy Process Framework is a structured approach that helps individuals and organizations understand, use, and communicate data effectively.
It provides a cycle of actions that ensure people not only have access to data, but also know how to interpret it, share insights, and make informed decisions.
It usually involves several steps—here you’re asking about six key ones:
1. Plan
- Meaning: Define your goals, scope, and approach for improving data literacy.
- Key Activities:
Identify business needs for data literacy
Set clear objectives (e.g., improve decision-making, reduce errors)
Decide who needs training and what skills are required - Example: A company plans a program to train marketing staff on reading analytics dashboards.
2. Communication
- Meaning: Share the purpose, benefits, and process of data literacy with all stakeholders.
- Key Activities:
Explain why data literacy is important
Keep everyone updated on progress
Use simple, jargon-free language - Example: An internal newsletter showing how better data skills saved money in a project.
3. Assess
- Meaning: Measure current data literacy levels in the organization or team.
- Key Activities:
Surveys or quizzes to test data skills
Identify skill gaps (e.g., understanding graphs, using tools)
Review how employees currently work with data - Example: HR conducts a test to see which departments struggle with data visualization.
Data Literacy Framework
4. Develop Culture
- Meaning: Create an environment where using and trusting data becomes a natural habit.
- Key Activities:
Encourage fact-based decision-making
Reward good data practices
Promote open data sharing between teams - Example: A company celebrates “Data-Driven Success Stories” in team meetings.
5. Prescriptive Learning
- Meaning: Provide targeted training and resources based on assessment results.
- Key Activities:
Offer workshops, online courses, or mentoring
Tailor learning to skill levels (beginner, intermediate, advanced)
Provide practice with real-world datasets - Example: New analysts get an Excel basics course, while senior staff learn predictive analytics.
6. Evaluate
- Meaning: Measure the impact of your data literacy program and make improvements.
- Key Activities:
Compare skill levels before and after training
Check if decision-making has improved
Gather feedback from participants - Example: After training, the sales team closes deals faster because they can interpret customer data better.
Simple Process Flow
Plan → Communication → Assess → Develop Culture →
Prescriptive Learning → Evaluate
This is cyclical—after evaluation, new planning begins based on updated needs.
What is Data Security?
- Definition: Data security is about protecting data from unauthorized access, misuse, or theft using tools and technologies.
- Goal: To ensure data remains confidential, accurate, and available only to authorized users.
Key Techniques:
- Encryption (coding data so it can’t be read by outsiders)
- Firewalls, antivirus software
- Access controls (passwords, biometrics, multi-factor authentication)
- Backups and recovery systems
What is Data Privacy?
- Definition: Data privacy is about how personal or sensitive data is collected, used, stored, and shared in a lawful and ethical way.
- Goal: To give individuals control over their personal data and ensure organizations handle it responsibly.
Key Practices:
- Following data protection laws (like GDPR, CCPA)
- Being transparent about data use
- Asking for user consent before collecting personal information
- Allowing users to update or delete their data
Data Security & Privacy
Why is Data Security Important?
- Prevents data breaches (hackers stealing information)
- Protects organizations from financial losses (fines, lawsuits, theft)
- Maintains trust and reputation with customers
- Ensures business continuity (data loss could stop operations)
Example: If a bank doesn’t secure its database, hackers could steal customer account details and cause huge losses.
Why is Data Privacy Important?
- Protects individual rights (like control over personal information)
- Builds customer trust (people are more willing to share data with companies that respect privacy)
- Helps organizations comply with laws and avoid penalties
- Prevents misuse of personal information (identity theft, scams, targeted manipulation)
Example: If a social media company shares user data without consent, it not only violates privacy but also risks losing users’ trust and facing legal action.
Simple Way to Remember
- Data Security = Protecting data from outsiders.
- Data Privacy = Respecting people’s rights over their data.
Both are important because without security, data can be stolen, and without privacy, data can be misused—even by those who legally have access.
What is Data?
👉 Data means raw facts, figures, or information that we collect for reference or analysis.
It can be in the form of numbers, words, symbols, pictures, or sounds.
Example:
45, “Apple”, ₹500, Delhi → all are data.
When we process data, it becomes information.
Textual Data
Textual data is data written in the form of words, letters, or characters.
Types of Textual Data:
Normal Data – Everyday written data in simple words. It can't be ordered or ranked.
Example: Names (Riya, Arun), Cities (Delhi, Mumbai), Male or female.Ordinal Data – It consists of categories that can be ordered.
Example: Job position(Manager, Supervisor, Employee), Education level
(👉 Both “Normal” and “Ordinary” data are qualitative – they describe things, not numbers.)
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| Data Type |
Numeric Data
Numeric data is data represented by numbers. It can be used for counting, measuring, or calculations.
Types of Numeric Data:
Discrete Data
Data that can only take whole numbers (countable).
Example: Number of students in class = 35, Number of books = 10.
❌ No fractions or decimals.Continuous Data
Data that can take any value, including fractions/decimals (measurable).
Example: Height = 5.6 ft, Weight = 48.3 kg, Temperature = 27.5°C.
✅ Can be broken into smaller values.
✅ Summary in one line:
Textual Data = Words (Normal & Ordinary)
Numeric Data = Numbers (Discrete = countable, Continuous = measurable)
AI Domains
👉 AI Domains are the different areas or fields in which Artificial Intelligence is applied.
Each domain focuses on solving a particular type of problem (like understanding language, analyzing images, or working with numbers).
1. Natural Language Processing (NLP)
Examples: Chatbots, Google Translate, Voice Assistants.
Type of Data:
Speech Data → Audio recordings, spoken language.
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| AI Domain & Data Type |
2. Computer Vision (CV)
Examples: Face recognition, Self-driving cars, Medical image analysis.
Type of Data:
Video Data → CCTV footage, traffic videos.
3. Statistical Data (in AI & ML)
Examples: Weather forecasting, Stock market prediction, Sports analytics.
Type of Data:
Categorical Data → Labels like “Yes/No”, “Spam/Not Spam”.
Continuous Data → Height, weight, temperature.
Discrete Data → Number of students, number of cars.
✅ Summary in Simple Words:
- NLP → Works with text & speech
- CV → Works with images & videos
- Statistical Data → Works with numbers & categories
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