Artificial Intelligence

CBSE Class 9 AI Data Literacy Notes (2026–27) | Complete Unit Notes with Examples

Class 9 · Artificial Intelligence

CBSE Class 9 Artificial Intelligence Notes – Unit 2: Data Literacy (2026–27)

Artificial Intelligence depends on one important resource - Data. However, collecting data alone is not enough. We must know how to understand it, analyse it, protect it, and present it effectively. These skills together are known as Data Literacy.

This unit helps students understand how data is collected, processed, interpreted, secured, and visualized so that better decisions can be made while building AI solutions.

Topics Covered

Data Literacy and its Impact
How to Become Data Literate
Data Security and Privacy
Best Practices for Cyber Security
Types of Data
Sources of Data
Best Practices for Acquiring Data
Features of Data and Data Preprocessing
Importance of Data Interpretation
Tools Used for Data Interpretation
Data Visualization
Visualization Using No-Code Tools
Creating Interactive Dashboards

PART 1 : Basics of Data Literacy

1. Data Literacy and Its Impact

What is Data Literacy?

Data Literacy is the ability to understand, interpret, and communicate with data.

Just as being "literate" means you can read and write a language, being data literate means you can understand raw facts, numbers, and observations and convert them into meaningful information for better decision-making.

A data-literate person does not simply look at numbers—they understand the story hidden inside the data.

The Data Pyramid (Stages of Working with Data)

To make meaningful decisions, we move step by step from raw data to wisdom. This journey is represented by the Data Pyramid.

Stage	Description
Data	Raw facts without context. Alone, data has very little meaning.
Information	Processed data that provides context and answers questions like Who, What, Where, and When.
Knowledge	Understanding how things happen by analysing information.
Wisdom	Using knowledge to make the correct decision or take action.

Practical Example – The Traffic Light

Stage	Traffic Light Example
Data	"Red, Traffic_Light_1"
Information	The south-facing traffic light on ABC Street has turned red.
Knowledge	The traffic signal in my direction has turned red.
Wisdom	I should stop my vehicle immediately to avoid an accident.

This example shows how raw facts gradually become useful decisions.

Practical Example – Film Ratings

Rahul watched three movies and rated them as:

Bad
Best
Average

A data-literate person understands that these are examples of qualitative (descriptive) data because they describe the quality of the movies instead of using numbers.

Impact of Data Literacy

Being data literate helps individuals make better decisions based on evidence rather than assumptions.

It enables people to:

Think critically.
Solve complex problems.
Identify patterns.
Innovate using data.
Communicate findings effectively.

Example – News Articles

Every news article tells a story. A data-literate person compares different news sources, verifies facts, and ranks the information from the most reliable to the least reliable before believing or sharing it.

This helps reduce the spread of misinformation and fake news.

2. How to Become Data Literate?

A data-literate person knows how to interact with data to understand the world around them and make informed decisions.

Practical Example – Buying a Video Game Online

Suppose you want to buy a video game online.

A data-literate buyer does not purchase the first game they see. Instead, they:

Apply filters such as Price: Low to High according to their budget.
Read customer ratings and reviews before making a purchase.
Check whether the game is compatible with their computer or mobile device.
Compare different products before making the final decision.

These actions demonstrate the practical use of data literacy in everyday life.

Data Literacy Process Framework

Step	Description
1. Plan	Define the goal and prepare an execution strategy.
2. Communicate	Explain the purpose of the goal to all stakeholders.
3. Assess	Evaluate the current level of data understanding using assessment tools.
4. Develop Culture	Build data skills into daily work and learning.
5. Prescriptive Learning	Provide learning resources suitable for different learners.
6. Evaluate	Measure progress regularly using appropriate metrics.

Since this is an iterative process, these steps continue repeatedly as learners improve their data skills.

3. Data Security and Privacy

The terms Data Privacy and Data Security are often used together, but they have different meanings. Both are essential for protecting digital information and building trustworthy AI systems.

What is Data Privacy?

Data Privacy refers to the proper collection, sharing, storage, and use of personal information. It ensures that sensitive information is accessed only by authorized people and is used only for the intended purpose.

In simple words, Data Privacy answers the question:

"Who is allowed to use my data and how?"

Example

Medical records contain highly confidential information. Only doctors, hospitals, or authorized healthcare professionals should be allowed to access them.

What is Data Security?

Data Security refers to protecting digital information from unauthorized access, theft, corruption, or cyber-attacks throughout its entire life cycle.

In simple words, Data Security answers the question:

"How do we protect our data from attackers?"

Difference Between Data Privacy and Data Security

Data Privacy	Data Security
Controls how data is collected, shared and used.	Protects data from theft, hacking and unauthorized access.
Focuses on users' rights and permissions.	Focuses on technical protection methods.
Concerned with confidentiality.	Concerned with protection against attacks.

How Can Data Privacy be Compromised?

Downloading unverified mobile applications that secretly collect personal information.
Accepting the "Terms of Service" without reading what information the application will collect.

Many users unknowingly allow apps to access their contacts, photos, location, microphone, and camera simply because they click "Accept" without reading the permissions.

Why Does Data Security Matter?

With increasing use of cloud computing and online services, cyber-attacks have become more common. If sensitive information is stolen, it can cause serious financial and personal damage.

1. Aadhaar Data Breach

One of India's biggest reported data breaches exposed the Aadhaar details of nearly 81.5 crore people.

2. Taj Hotel Data Breach

A reported cyberattack placed the personal information of approximately 1.5 million customers at risk.

3. AIIMS Ransomware Attack

The ransomware attack on AIIMS blocked access to critical hospital records, affecting healthcare services.

These incidents highlight why strong data security measures are essential.

Best Practices for Maintaining Data Privacy

Developers and organizations should always follow responsible data collection practices.

Collect only the data that is absolutely necessary.
Always obtain user consent before collecting personal information.
Clearly explain why the data is being collected.
Store personal information securely.
Never share private information without permission.

4. Best Practices for Cyber Security

Cyber Security refers to protecting computers, mobile devices, networks, software, and digital information from cyber-attacks and unauthorized access.

The "DO's" for Cyber Safety

1. Use Strong Passwords

Create passwords using a combination of:

Uppercase letters
Lowercase letters
Numbers
Special characters

Strong passwords are difficult for hackers to guess.

2. Enable Two-Factor Authentication (2FA)

Two-Factor Authentication provides an additional layer of security by requiring a second verification method, such as an OTP sent to your mobile phone.

3. Use Secure Websites

Always prefer websites that begin with:

https://

The "S" stands for Secure and indicates that information is encrypted while being transmitted.

4. Keep Software Updated

Regularly update:

Operating System
Web Browser
Antivirus Software
Applications

Software updates fix security vulnerabilities that hackers may exploit.

5. Lock Your Device

Always lock your computer, laptop, or mobile phone whenever you leave it unattended.

This simple habit protects personal information from unauthorized access.

6. Report Cyberbullying

If you experience online bullying or receive threatening messages, immediately report the incident to parents, teachers, or another trusted adult.

The "DON'Ts" for Cyber Safety

1. Do Not Share Personal Information

Home address
Phone number
School details
Password
Bank information

Never post this information on public websites or social media.

2. Avoid Sharing Photos with Strangers

Never send personal photographs to unknown people or upload sensitive images on social media platforms.

3. Do Not Open Suspicious Emails

Avoid opening emails, attachments, or links received from unknown senders because they may contain malware or phishing attacks.

4. Ignore Suspicious Requests

Banks and trusted organizations never ask for passwords, OTPs, or PIN numbers through email or phone calls.

Never share confidential information with unknown persons.

5. Respect Copyright Laws

Never copy copyrighted software illegally or distribute pirated applications.

Similarly, avoid using abusive or offensive language while interacting online because responsible digital behaviour is also an important part of cyber safety.

Quick Safety Checklist

Always Do ✔	Never Do ✘
Use strong passwords.	Share passwords.
Enable Two-Factor Authentication.	Open unknown email attachments.
Visit HTTPS websites.	Share personal information publicly.
Keep software updated.	Download unverified applications.
Lock your device.	Use pirated software.
Report cyberbullying.	Respond to suspicious messages.

PART 2 : Acquiring Data, Processing and Interpreting Data

Artificial Intelligence systems learn from data. Therefore, collecting high-quality data is one of the most important steps while building an AI solution. This section explains the different types of data, where data comes from, how it should be collected, and how it is interpreted to make meaningful decisions.

1. Types of Data

Data is the foundation of every Artificial Intelligence system. In our daily lives, data generally exists in three major forms.

1. Textual Data (Qualitative Data)

Textual Data consists of words, sentences, and descriptions. It is mainly used in the field of Natural Language Processing (NLP), where computers learn to understand human language.

Examples

Emails
Books
SMS messages
Social media posts
Customer reviews
Newspaper articles

Practical Example

When you type the question

"Which is a good park nearby?"

into Google Search or another search engine, the AI analyses the text using Natural Language Processing and returns relevant results.

2. Numeric Data (Quantitative Data)

Numeric Data consists of numbers and measurements. It is mainly used in Statistical Data Analysis for identifying patterns and making predictions.

Examples

Cricket scores
Restaurant bills
Student marks
Monthly sales
Population statistics

Continuous Data

Continuous Data can take any value within a range.

Examples:

Height
Weight
Temperature
Distance
Speed

Discrete Data

Discrete Data contains only whole numbers. It cannot be divided into fractions.

Examples:

Number of students in a class
Number of books in a library
Number of computers in a laboratory

For example, a class can have 40 students, but it cannot have 40.5 students.

3. Visual Data

Visual Data consists of images and videos. It is mainly used in the field of Computer Vision (CV).

Examples

Photographs
CCTV footage
X-ray images
Medical scans
Satellite images

Computer Vision systems analyse visual data to recognize faces, identify objects, detect traffic signs, and monitor public safety.

2. Sources of Data

Data can be collected from two main types of sources.

Primary Data Sources

Primary Data is collected directly by the person or organization for a specific purpose.

Examples

Surveys
Questionnaires
Interviews
Experiments
Focus Groups
Sensors

Primary data is generally more reliable because it is collected first-hand.

Secondary Data Sources

Secondary Data is information that has already been collected by someone else and is available for public use.

Reliable Sources

Kaggle – A popular community for data scientists where datasets are freely available.
Google Dataset Search – Helps users search datasets from different organizations.
data.gov.in – Official Government of India Open Data Portal.

Using reliable sources improves the quality and accuracy of AI projects.

3. Best Practices for Acquiring Data

Collecting data is a planned process. Three important stages of acquiring data.

Step 1 – Data Discovery

Data Discovery means searching for and downloading existing datasets that are already available.

Practical Example

Suppose developers want to build a self-driving car. Instead of taking millions of photographs themselves, they may download existing road image datasets from reliable repositories.

Step 2 – Data Augmentation

Sometimes the available dataset is too small. Data Augmentation increases the amount of data by making small changes to existing data.

Practical Example

One image of a dog can be converted into multiple images by changing:

Brightness
Colour
Contrast
Rotation
Image orientation

These modified images help AI models learn better without collecting completely new data.

Step 3 – Data Generation

When suitable datasets are unavailable, developers generate new data using sensors or recording devices.

Practical Example

A temperature sensor installed inside a building continuously records temperature readings, creating a completely new dataset.

Important Note – Web Scraping

Collecting information automatically from websites using software is called Web Scraping.

Although web scraping itself is a common technique, using data from a website without the owner's permission is illegal if the website does not allow such usage.

The Ethical Checklist while Acquiring Data

Developers should always ensure that data is collected responsibly.

Principle	Description
Bias	Avoid favouring one group over another.
Consent	Always obtain permission before collecting personal information.
Transparency	Clearly explain how collected data will be used.
Anonymity	Protect the identity and privacy of individuals.

4. Features of Data and Data Preprocessing

Data Features

Data Features are the characteristics or properties that describe a dataset.

Example

In a student database, common features include:

Name
Age
Class
Grade

Independent Features

Independent Features are the input variables used by an AI model to make predictions.

Example: Study Hours

Dependent Features

Dependent Features are the outputs or results that the AI model predicts.

Example: Pass or Fail

Data Preprocessing (Data Cleaning)

Before data is used for training an AI model, it must be cleaned and standardized.

Practical Example

Suppose you have collected 500 photographs for an AI project. Some images are very large, while others are very small.

Before training the AI model, all images should be resized to the same dimensions so that the model receives consistent input.

Characteristics of Good Data

Well-structured
Accurate
Relevant
Free from duplicate records
Easy to understand

5. Importance of Data Interpretation

Collecting data is only the first step in an AI project. The real value of data comes from Data Interpretation, which is the process of analysing processed data to understand its meaning and make informed decisions.

Data interpretation converts numbers, observations, and patterns into useful knowledge that can guide individuals and organizations.

Why is Data Interpretation Important?

1. Informed Decision Making

A decision is only as good as the information on which it is based. Proper interpretation of data enables people to make logical, evidence-based decisions instead of relying on assumptions.

Practical Example

A school studies the average height of students before purchasing classroom furniture. Based on the interpreted data, desks and chairs are designed to suit the students comfortably.

2. Reduced Cost

Interpreting data helps organizations identify unnecessary expenses and improve efficiency.

Practical Example

A restaurant analyses customer reviews and discovers that one particular dish consistently receives poor feedback. By removing that dish from the menu, the restaurant reduces wastage of ingredients and saves money.

3. Identifying Customer Needs

Data interpretation helps organizations understand exactly what customers or users need.

For example, an online shopping company analyses customer purchase history to recommend products that customers are more likely to buy.

6. Tools Used for Data Interpretation

Different methods and software tools are available to analyse and present data depending on the type of information being studied.

Qualitative Methods

Qualitative methods focus on human experiences, opinions, behaviour, and emotions rather than numerical values.

Examples

One-to-One Interviews
Observation
Focus Group Discussions
Longitudinal Studies (studying the same people over a long period)

These methods help researchers understand people's thoughts, preferences, and experiences.

Quantitative Methods

Quantitative methods deal with numbers, measurements, and statistical analysis.

Examples

Polls
Surveys
Assessments
Tests

These methods are useful when analysing numerical trends and comparing measurable information.

Methods of Presenting Data

Once data has been analysed, it should be presented in a format that is easy to understand.

1. Textual Presentation

Data is presented using simple paragraphs or written descriptions.

This method is suitable when the amount of data is small.

2. Tabular Presentation

Data is organised systematically into rows and columns.

Tables make comparison easy and improve readability.

Example: A spreadsheet showing students' marks.

3. Graphical Presentation

Graphs make large datasets easier to understand by representing information visually.

Common Graphs

Graph	Purpose
Bar Graph	Compare quantities between different categories.
Pie Chart	Show parts of a whole.
Line Graph	Show changes or trends over time.

Graphs make it much easier to identify patterns than reading long tables of numbers.

Software Used for Data Interpretation

Popular tools used for analysing and presenting data.

Tableau – Professional visual analytics software.
MS Excel – Widely used for tables, formulas and charts.
Datawrapper – Online tool for creating interactive charts and maps.

PART 3 : Project – Interactive Data Dashboard & Presentation

After collecting and interpreting data, the next step is presenting the information visually so that others can easily understand the story hidden within the data.

1. Data Visualization and Its Importance

What is Data Visualization?

Data Visualization is the graphical representation of information using charts, graphs, maps and other visual elements.

Instead of reading thousands of numbers, people can quickly understand patterns through visual representations.

The Library Book Analogy

When you visit a library and pick up a book, you normally do not read every page immediately.

Instead, you first:

Look at the book cover.
Read the back cover description.
Skim through a few pages.

Within a few minutes, you decide whether the book matches your interests.

Data Visualization works in exactly the same way. Instead of reading thousands of numbers individually, graphs allow us to quickly understand the overall story hidden inside the data.

Why is Data Visualization Important for AI?

Quick Understanding: Humans understand pictures much faster than large tables of numbers.
Pattern Recognition: Graphs help identify relationships, trends and hidden patterns.
Choosing AI Models: During the Data Exploration stage, visualization helps developers decide which AI model is most suitable.
Effective Communication: Charts and dashboards present findings clearly to stakeholders.

Practical Example – Predicting Billboard Top 10 Songs

Suppose an AI developer wants to predict which songs are likely to reach the Billboard Top 10.

The dataset may include features such as:

Singer Popularity
Music Genre
Song Length
Release Year

After plotting a graph between Singer Popularity and Song Success, the developer may observe that singers with greater popularity have a higher probability of reaching the Top 10 list.

This pattern helps developers build better AI prediction models.

2. Visualization Using No-Code Tools

Students do not need programming knowledge to create professional dashboards.

No-Code tools provide a drag-and-drop environment for building charts and interactive visualizations.

No-Code Tools Mentioned in the Handbook

Tableau Public
MS Excel
Datawrapper

These tools are mainly used during the Data Exploration stage of the AI Project Cycle.

3. Creating a Simple & Interactive Chart (Step-by-Step)

Step 1 – Prepare the Dataset

Before creating any chart, data must first be collected and organized properly.

Create a spreadsheet containing information about your favourite songs with the following data features:

Song Name	Album	Artist	Genre	Year	Song Length
Example	Album Name	Artist Name	Pop	2025	3:45

The handbook recommends collecting data for approximately 5–10 songs.

Step 2 – Connect to the Data

Open Tableau Public.

On the home screen:

Select Microsoft Excel.
Browse and select the spreadsheet containing your song data.
Import the dataset into Tableau.

Once imported successfully, all the data features become available for creating charts.

Step 3 – Create a Bar Chart

To visualize the number of songs in each genre:

Drag the Genre field to the Columns shelf.
Drag Sample (Count) to the Rows shelf.

Immediately, Tableau creates a Bar Chart showing the number of songs belonging to each music genre.

This allows users to compare categories quickly and identify the most common genre.

Step 4 – Add Colours to the Chart

A colourful visualization is easier to understand than a plain chart.

To colour the chart:

Drag the Genre field once again.
Drop it onto the Color option inside the Marks card.

Now every genre automatically receives its own unique colour, making comparison much easier.

Step 5 – Create a Packed Bubble Chart

The handbook next explains how to convert the same data into an interactive Packed Bubble visualization.

Right-click the worksheet tab.
Select Duplicate.
Click the Show Me button located in the top-right corner.
Select the Packed Bubbles chart.

The bar chart is automatically transformed into colourful circles.

The largest bubble represents the most frequently occurring music genre in the dataset.

Step 6 – Format the Labels

To make the dashboard more attractive and easier to read:

Click the Label option.
Select the preferred font.
The handbook suggests using the Chalkboard font.
Set the font size to 12.

Proper formatting improves readability and creates a professional-looking dashboard.

Role of Tableau in the AI Project Cycle

Tableau is mainly used during the:

Data Exploration Stage

It helps developers understand data patterns before selecting or building an AI model.

Quick Revision

Data Literacy means understanding, interpreting and communicating with data.
The Data Pyramid consists of:
- Data
- Information
- Knowledge
- Wisdom
The Traffic Light example explains how raw data becomes useful knowledge.
Data Literacy helps people think critically and make evidence-based decisions.
The Data Literacy Process Framework has six steps: Plan, Communicate, Assess, Develop Culture, Prescriptive Learning and Evaluate.
Data Privacy controls how personal information is collected and shared.
Data Security protects data from theft and cyber-attacks.
Examples of major data breaches include:
- Aadhaar Data Leak
- Taj Hotel Data Breach
- AIIMS Ransomware Attack
Always follow Cyber Security best practices:
- Strong Passwords
- Two-Factor Authentication
- HTTPS Websites
- Software Updates
- Lock Devices
- Report Cyberbullying
The three major types of data are:
- Textual Data
- Numeric Data
- Visual Data
Numeric Data is classified into:
- Continuous Data
- Discrete Data
Data may come from:
- Primary Sources
- Secondary Sources
The three stages of acquiring data are:
- Data Discovery
- Data Augmentation
- Data Generation
Developers should always ensure:
- Bias-free data
- User Consent
- Transparency
- Anonymity
Data Preprocessing removes duplicate, inconsistent and poorly structured data.
Data Interpretation helps organizations make informed decisions and reduce costs.
Presentation formats include:
- Textual
- Tabular
- Graphical
Common graphs include:
- Bar Graph
- Pie Chart
- Line Graph
Popular No-Code tools include:
- Tableau Public
- MS Excel
- Datawrapper
Data Visualization helps identify trends, relationships and hidden patterns.
The "Favorite Songs" project demonstrates how to build interactive dashboards in Tableau.
Tableau is mainly used during the Data Exploration stage of the AI Project Cycle.

Exam Tips

Remember the complete Data Pyramid in the correct order: Data → Information → Knowledge → Wisdom.
Revise both practical examples:
- Traffic Light
- Film Ratings
Learn the six steps of the Data Literacy Process Framework.
Understand the difference between Data Privacy and Data Security.
Memorize the three real-life cyber security incidents:
- Aadhaar Data Leak
- Taj Hotel Data Breach
- AIIMS Ransomware Attack
Differentiate clearly between:
- Primary and Secondary Data Sources
- Continuous and Discrete Data
- Qualitative and Quantitative Data
Remember the three stages of acquiring data: Discovery → Augmentation → Generation.
Study the ethical checklist: Bias, Consent, Transparency and Anonymity.
Learn the examples used for Data Interpretation:
- School Furniture
- Restaurant Menu Analysis
Practice identifying the correct graph for different situations.
Remember the Tableau workflow:
- Import Excel Data
- Create Bar Chart
- Apply Colours
- Create Packed Bubble Chart
- Format Labels
Important CBSE Questions:
- Which stage of the AI Project Cycle uses Tableau?
  Answer: Data Exploration
- Is "Year" a Qualitative or Quantitative feature?
  Answer: Quantitative
- Why is Data Visualization important?
  Answer: It helps humans quickly understand trends and extract meaningful insights from data.

Conclusion

Data is the foundation of every Artificial Intelligence system, but data alone has little value unless it is understood, interpreted, protected, and presented effectively. By becoming data literate, students learn how to make evidence-based decisions, protect sensitive information, analyse different types of data, and communicate insights using interactive visualizations. These skills are essential for developing reliable, ethical, and intelligent AI solutions in the future.