🌟 Introduction
In today’s world, data is the new oil — and Artificial Intelligence (AI) is the engine that runs on it.
Every AI system, from your voice assistant to a weather prediction app, relies on Data Science to make smart, evidence-based decisions.
In Class 7 Artificial Intelligence, understanding Data Science in AI is the first step toward becoming a future innovator.
This chapter helps students explore how data is collected, analyzed, and transformed into powerful insights that drive intelligent machines.
💡 Focus Keyword Placement: Data Science in Artificial Intelligence Class 7 is the foundation of modern smart systems that learn, adapt, and predict outcomes using data.
TABLE OF CONTENTS
📘 What is Data Science in Artificial Intelligence?
🔹 Definition:
Data Science is the process of collecting, organizing, analyzing, and interpreting large amounts of data to make meaningful decisions.
In the context of Artificial Intelligence, data science gives machines the knowledge and patterns they need to make predictions, recognize trends, and automate tasks.
🧠 Example:
When you use Google Maps, it uses data from millions of users (speed, location, time) to predict the best route — that’s Data Science in AI at work!
📊 Why It Matters:
Without data, AI cannot learn.
Without Data Science, AI cannot think.
Together, they make intelligent systems that shape our future.
🔍 2.1 Introduction: Data Science & Its Role in AI
Data Science is the backbone of Artificial Intelligence.
It helps machines learn from experience, just as humans do.
Here’s how it connects:
- Data Science provides raw data (facts, figures, and information).
- Artificial Intelligence uses algorithms to find patterns in that data.
- Together, they produce insights and actions — like predictions, classifications, or recommendations.
🔹 Example:
In healthcare, AI uses data from thousands of patients to predict diseases early.
In education, AI tools analyze student performance to recommend personalized lessons.
📘 Key takeaway:
Data Science in Artificial Intelligence helps convert data into decisions.
💾 2.2 Data and Its Types
To understand Data Science, we must first understand what data means.
🔹 Definition of Data:
Data refers to facts, figures, or information collected from various sources that can be processed and analyzed.
🔍 Types of Data in AI:
| Type | Description | Example |
|---|---|---|
| Structured Data | Organized in rows and columns, easy to store in databases. | Spreadsheets, student marks, sales records. |
| Unstructured Data | Raw and unorganized data without a fixed format. | Emails, social media posts, videos, audio files. |
| Semi-Structured Data | A mix of structured and unstructured data. | JSON files, HTML pages, XML documents. |
📘 Figure 2.1 – Types of Data
+-------------------------+
| DATA |
+-----------+--------------+
|
------------------------------------------------
| | |
Structured Data Unstructured Data Semi-Structured Data
(Tables, Numbers) (Images, Audio, Text) (XML, JSON Files)
💡 Classroom Tip:
Ask students to classify examples like “weather reports,” “tweets,” and “videos” under the correct data type.
🏗️ 2.3 Big Data – The Power of Massive Information
Today, organizations handle billions of data points daily — this is called Big Data.
🔹 Definition:
Big Data refers to extremely large datasets that are too complex for traditional tools to handle.
AI systems use Big Data to find patterns humans can’t see.
🔹 The 5Vs of Big Data:
| V | Meaning | Explanation |
|---|---|---|
| Volume | Quantity of data | Millions of gigabytes created daily. |
| Velocity | Speed of data generation | Social media posts, stock updates every second. |
| Variety | Types of data | Images, videos, sensors, transactions. |
| Veracity | Accuracy & reliability | Ensuring data is correct and unbiased. |
| Value | Usefulness | Extracting insights that help in decision-making. |
📘 Figure 2.2 – The 5Vs of Big Data
+-------------------------------------------+
| BIG DATA |
+-------------------------------------------+
| Volume | Velocity | Variety | Veracity | Value |
💡 Example:
Netflix uses Big Data to study what people watch — and uses AI to recommend what they’ll love next.
🔄 2.4 Data Science Workflow
Every Data Science project follows a systematic workflow — a step-by-step process that converts raw data into knowledge.
🔹 Workflow Steps:
- Data Collection:
Gathering data from sensors, surveys, internet sources, or databases.
🧩 Example: Collecting weather data from satellite sensors. - Data Cleaning:
Removing errors, duplicates, and incomplete records to make data usable.
🧩 Example: Deleting duplicate survey responses. - Data Analysis:
Using statistical or AI tools to find patterns or trends.
🧩 Example: Studying which areas receive more rainfall. - Data Visualization:
Presenting results using graphs, charts, or dashboards for easy understanding.
🧩 Example: Showing temperature trends over time using a line graph.
📘 Figure 2.3 – Data Science Workflow
[ Data Collection ] → [ Data Cleaning ] → [ Data Analysis ] → [ Data Visualization ]
💡 Remember:
This process allows AI systems to turn data into useful, human-understandable results.
🧩 2.4.1 Key Components of Data Science
Data Science combines several fields to help AI understand data deeply.
🔹 Main Components:
| Component | Description | Example |
|---|---|---|
| Data Collection | Gathering information from various sources. | IoT devices, sensors, online databases. |
| Data Processing | Preparing data for analysis. | Formatting, filtering, and cleaning. |
| Machine Learning | Teaching machines to find patterns in data. | Predicting student grades or stock trends. |
| Data Visualization | Turning data into charts, graphs, or dashboards. | Pie charts showing population growth. |
| Statistics | Understanding data trends mathematically. | Calculating averages, percentages. |
🧠 Real-Life Example:
- Google Search: Uses machine learning to predict what users will search next.
- E-commerce Sites: Use data analytics to suggest similar products.
- Education Apps: Track learning patterns to give personalized practice.
💡 2.5 Importance of Data Science in Artificial Intelligence
Data Science in AI helps machines learn, reason, and improve.
It makes AI smarter, faster, and more accurate over time.
🔹 How Data Science Empowers AI:
- Learning from Experience: AI systems train on past data.
- Making Predictions: Data Science helps AI forecast outcomes.
- Improving Accuracy: The more data AI gets, the more precise its results.
- Automating Decisions: AI can make quick, data-backed choices.
- Supporting Innovation: Enables smart systems — from chatbots to self-driving cars.
🧠 Example Scenarios:
- AI predicting weather or health trends.
- AI recommending movies on OTT platforms.
- AI optimizing energy use in smart homes.
📘 Figure 2.4 – Role of Data Science in AI
DATA → PATTERN → LEARNING → DECISION → ACTION
💡 In simple words:
Data Science gives AI its “brainpower.”
🎮 2.6 Data Science Games (Fun with AI)
Learning Data Science can be exciting!
Here are some interactive classroom activities that make data learning fun.
🕹️ Activity 1: Weather Data Detective
Goal: Understand data collection and analysis.
Steps:
- Collect daily temperature data for 10 days.
- Plot it on a graph.
- Predict the next 2 days’ temperature trend.
💡 Skills learned: Pattern recognition, data visualization.
🎲 Activity 2: AI Market Game
Goal: Experience data-driven decision-making.
Steps:
- Students act as “data scientists” selling fruits.
- Each gets random “sales data.”
- Analyze to find which fruit sells most.
💡 Skills learned: Data analysis and decision making.
🤖 Activity 3: Smart City Simulation
Goal: Use data to optimize resources.
Steps:
- Simulate a city’s energy usage.
- AI predicts peak hours to save power.
💡 Skills learned: Systems thinking and sustainability.
🧭 Connecting Data Science to Sustainability & SDGs
Data Science in AI also contributes to Sustainable Development Goals (SDGs):
| SDG | Contribution of AI & Data Science |
|---|---|
| 🌾 Zero Hunger (SDG 2) | Predicting crop yield, weather for farmers. |
| 🌞 Clean Energy (SDG 7) | Optimizing solar power and energy grids. |
| 🌍 Climate Action (SDG 13) | Analyzing environmental data for prevention. |
| 🎓 Quality Education (SDG 4) | Personalized learning paths for students. |
🌈 Chapter Summary
- Data Science is vital for AI decision-making.
- Data can be structured, unstructured, or semi-structured.
- Big Data is defined by the 5Vs — Volume, Velocity, Variety, Veracity, Value.
- The Data Science Workflow involves collection, cleaning, analysis, and visualization.
- AI depends on Data Science for learning and predictions.
- Students can practice through Data Science Games and simulations.
🧩 Exercise Section
🌟 1. Multiple Choice Questions (MCQs)
Choose the correct option.
- Data Science in Artificial Intelligence helps machines to —
A. Perform manual tasks
B. Learn from data and make decisions
C. Create art and music
D. Work without algorithms
Answer: B - Which of the following best defines Data Science?
A. Creating computer hardware
B. Collecting and analyzing data to make decisions
C. Writing stories using AI
D. Building computer games
Answer: B - Which type of data is organized in rows and columns?
A. Structured Data
B. Unstructured Data
C. Semi-Structured Data
D. Visual Data
Answer: A - Emails and social media posts are examples of —
A. Structured Data
B. Semi-Structured Data
C. Unstructured Data
D. Tabular Data
Answer: C - The 5Vs of Big Data include Volume, Velocity, Variety, Veracity, and —
A. Vision
B. Value
C. Validation
D. Visualization
Answer: B - In the Data Science Workflow, the process of removing duplicates and errors is called —
A. Data Analysis
B. Data Cleaning
C. Data Collection
D. Data Visualization
Answer: B - Machine Learning in Data Science helps AI to —
A. Store data only
B. Find patterns and learn from data
C. Draw pictures from data
D. Erase irrelevant data
Answer: B - Netflix uses Big Data to —
A. Build websites
B. Recommend movies
C. Play video games
D. Create subtitles
Answer: B - The main purpose of Data Visualization is to —
A. Encrypt information
B. Present data using graphs and charts
C. Delete unnecessary data
D. Train algorithms
Answer: B - The “brainpower” behind AI that enables it to learn from data is —
A. Robotics
B. Data Science
C. Coding
D. Statistics
Answer: B
✅ Answer Key (MCQs)
1-B | 2-B | 3-A | 4-C | 5-B | 6-B | 7-B | 8-B | 9-B | 10-B
✍️ 2. Fill in the Blanks
- Data Science helps AI systems to make __________ decisions.
Answer: data-driven - __________ is the process of collecting, organizing, and analyzing data.
Answer: Data Science - Structured data is stored in the form of __________ and columns.
Answer: rows - Videos and audio files are examples of __________ data.
Answer: unstructured - The term “Big Data” refers to __________ datasets that are too complex for traditional tools.
Answer: extremely large - The five characteristics of Big Data are known as the __________.
Answer: 5Vs - The process of correcting and cleaning data is called __________.
Answer: Data Cleaning - Data Visualization converts information into __________ or charts.
Answer: graphs - Machine Learning helps AI find __________ in data.
Answer: patterns - Without data, AI cannot __________.
Answer: learn
⚖️ 3. Assertion–Reason Questions
(Choose the correct option)
A — Both Assertion and Reason are true, and Reason is the correct explanation.
B — Both are true, but Reason is not the correct explanation.
C — Assertion is true, but Reason is false.
D — Assertion is false, but Reason is true.
- Assertion: Big Data includes structured, unstructured, and semi-structured data.
Reason: AI only works on small datasets.
Answer: C - Assertion: Data Cleaning is an important step before analysis.
Reason: Clean data ensures accurate and reliable results.
Answer: A - Assertion: Data Visualization helps people understand complex data easily.
Reason: It presents results using images and graphs.
Answer: A - Assertion: Machine Learning allows AI systems to learn from data automatically.
Reason: It reduces the need for human intervention.
Answer: A - Assertion: Data Science has no relation to Artificial Intelligence.
Reason: AI and Data Science work independently.
Answer: D
💬 4. Very Short Answer Type Questions (VSAQs)
(Answer in 1–2 lines)
- Define Data Science.
Answer: Data Science is the process of collecting, analyzing, and interpreting data to make informed decisions. - What are the three types of data?
Answer: Structured, Unstructured, and Semi-Structured data. - What is Big Data?
Answer: Extremely large and complex datasets that traditional tools cannot handle. - List any two Vs of Big Data.
Answer: Volume and Velocity. - What is Data Cleaning?
Answer: The process of removing errors and inconsistencies from data. - Mention one example of Data Visualization.
Answer: A line graph showing temperature changes. - What does Machine Learning do?
Answer: It helps AI find patterns and make predictions using data. - Why is Data Science important for AI?
Answer: It provides the data AI needs to learn and make decisions. - Give one real-life example of Data Science in AI.
Answer: Netflix recommending shows based on user viewing data. - What does “Veracity” in Big Data mean?
Answer: The accuracy and reliability of data.
🧩 5. Short Answer Type Questions (SAQs)
(Answer in 2–3 sentences)
- Explain the relationship between Data Science and Artificial Intelligence.
Answer: Data Science provides the data and patterns, while AI uses them to learn, predict, and act. Together, they make smart systems possible. - Differentiate between Structured and Unstructured Data.
Answer: Structured data is organized in tables (like marksheets), while unstructured data includes raw formats like videos or social media posts. - What are the 5Vs of Big Data?
Answer: Volume, Velocity, Variety, Veracity, and Value — representing the size, speed, types, accuracy, and usefulness of data. - Describe the steps in the Data Science Workflow.
Answer: It includes Data Collection, Data Cleaning, Data Analysis, and Data Visualization. - What is the purpose of Data Cleaning in AI projects?
Answer: It ensures that data used by AI systems is accurate, complete, and ready for analysis. - How does Data Visualization help decision-making?
Answer: It converts data into easy-to-understand visuals like graphs, helping people identify trends quickly. - Mention one classroom activity that helps understand Data Science.
Answer: Weather Data Detective — collecting temperature data and predicting future trends. - How does Data Science contribute to Sustainable Development Goals (SDGs)?
Answer: It helps in predicting weather for farmers (SDG 2), optimizing energy (SDG 7), and climate monitoring (SDG 13). - Explain the importance of Machine Learning in Data Science.
Answer: It enables AI systems to learn from data and improve performance without being explicitly programmed. - Give two examples of real-world applications of Data Science in AI.
Answer: Health diagnosis systems and personalized learning in education apps.
🧠 6. Long Answer Type Questions (LAQs)
(Answer in 5–8 sentences)
- Define Data Science and explain its role in Artificial Intelligence.
Answer: Data Science involves collecting, organizing, and analyzing data to gain insights. In AI, it provides the information that algorithms use to learn and make decisions. Without Data Science, AI cannot function effectively — it is the foundation that powers smart technologies. - Discuss the types of data with examples.
Answer: Data is of three types — Structured (tables like exam scores), Unstructured (images, videos, emails), and Semi-Structured (JSON, XML). Each type helps AI process different kinds of information for smarter solutions. - Explain the 5Vs of Big Data with examples.
Answer: Volume (huge data size), Velocity (speed of data creation), Variety (different formats), Veracity (data accuracy), and Value (usefulness). For instance, social media data changes every second, showing high velocity and variety. - Describe the steps of the Data Science Workflow with one example.
Answer: The workflow includes Data Collection, Data Cleaning, Data Analysis, and Data Visualization. Example: Weather data collected from satellites is cleaned, analyzed for patterns, and visualized in charts to forecast rain. - How does Data Science help in achieving Sustainable Development Goals (SDGs)?
Answer: Data Science supports global goals by providing actionable insights — predicting droughts (Zero Hunger), optimizing energy use (Clean Energy), and tracking pollution (Climate Action). It connects technology with sustainability.
🌍 7. Source-Based / Case-Based Assessment Questions
Case Study 1: AI in Education
Source Extract:
“An educational app uses Data Science and AI to track students’ progress. It studies performance patterns and suggests personalized learning activities. Teachers use this data to plan better lessons and provide individual support.”
Questions:
- Which AI domain is used in this case?
- How does Data Science help improve learning outcomes?
- Mention one benefit of personalized learning.
- Identify one possible challenge of using such systems.
Answer Key:
- Data Science in AI.
- By analyzing student performance data to identify strengths and weaknesses.
- It adapts lessons to each student’s learning pace.
- Data privacy and misuse concerns.
Case Study 2: Smart Farming with AI
Source Extract:
“Farmers now use AI systems with sensors to monitor soil moisture and crop health. Data collected helps predict when to irrigate or use fertilizer. This saves water and improves yield.”
Questions:
- What type of data is collected in this example?
- Which SDG does this support?
- How does this system promote sustainability?
- Mention one limitation of relying on AI in agriculture.
Answer Key:
- Structured and semi-structured data (sensor readings, weather info).
- Zero Hunger (SDG 2).
- Reduces waste and optimizes resource use.
- High setup cost or need for reliable internet.
Case Study 3: Big Data and Online Shopping
Source Extract:
“Online shopping platforms collect data from millions of users — what they search, buy, or review. AI uses this Big Data to suggest similar products and improve the shopping experience.”
Questions:
- Which of the 5Vs of Big Data is highlighted here?
- How does AI use this data to make recommendations?
- Mention one ethical issue related to online data collection.
- Explain how this improves business decisions.
Answer Key:
- Volume and Variety.
- By finding patterns in user behavior.
- Privacy and data misuse concerns.
- Helps predict demand and plan stock.
Case Study 4: Data Science in Climate Action
Source Extract:
“Environmental scientists use AI and Data Science to study climate data from satellites. These systems analyze temperature, pollution, and ocean patterns to predict disasters and warn communities early.”
Questions:
- Which Sustainable Development Goal is supported here?
- What type of data is being analyzed?
- Mention one benefit of early warning systems.
- How does this showcase the link between Data Science and sustainability?
Answer Key:
- Climate Action (SDG 13).
- Big Data — temperature, pollution, and environmental readings.
- Saves lives and reduces disaster impact.
- It uses data-driven insights to protect the planet.
❓ FAQ – Data Science in Artificial Intelligence Class 7
Q1. What is Data Science in AI?
Answer: It is the process of collecting, cleaning, analyzing, and visualizing data to help AI systems make smart decisions.
Q2. Why is Data Science important for AI?
Answer: Because AI learns and improves through data — the more data it gets, the smarter it becomes.
Q3. What are the 5Vs of Big Data?
Answer: Volume, Velocity, Variety, Veracity, and Value.
Q4. How is data used in everyday AI applications?
Answer: From Google Search to Netflix recommendations — all AI systems rely on analyzing huge amounts of data.
Q5. What activities help students learn Data Science?
Answer: Graph plotting, pattern prediction, and simulation games like “Weather Detective” or “AI Market Game.”
🧠 Internal Links (For Educational Websites)
- Domains of AI – Class 7 Chapter 1
- Artificial Intelligence Projects for Students
- Digital Citizenship and AI Ethics
🌍 External DoFollow Links (Authoritative Sources)
🧾 Conclusion
Data Science in Artificial Intelligence Class 7 builds a strong foundation for future-ready learners.
It teaches how to think analytically, act responsibly, and use data to solve global problems.
By exploring data types, Big Data, and real-world workflows, students gain essential skills to thrive in a digital, AI-powered world.
With curiosity and creativity, the young data scientists of today will become the AI innovators of tomorrow. 🚀