(FINAL) TIMETABLE FOR SSC & HSSC PRACTICAL

 

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REVISION- 1.COMPUTER SYSTEM

 

Topic 1.1: Understanding of Natural and Artificial Systems

• 1.1.1: Differentiate between natural and artificial systems with real-world examples.

Topic 1.2: Computational Architecture

• 1.2.1: Define various input and output devices, including keyboards, touchscreen, pointing devices, biometric scanners, sensors, recognition systems, audio devices, display screens, printers, plotters, cutters, and actuators.

• 1.2.2: Identify the use of types of sensors, i.e., temperature, moisture, light, infra-red, pressure, sound/acoustic, gas and pH.

• 1.2.3: Describe primary and secondary storage devices based on location, cost, capacity, access time, data processing method, and storage technology such as semiconductor (SSD), magnetic (HDD), and optical.

• 1.2.4: Illustrate the Von Neumann Architecture using a block diagram.

• 1.2.5: Illustrate the system bus and its types, including data bus, address bus and control bus, along with their roles in computer architecture using a diagram.

Topic 1.3: Memory Measurement Units

• 1.3.1: Distinguish among the memory measuring units such as bits, bytes, kilobytes, megabytes, and gigabytes in computer memory.

• 1.3.2: Compare the types of primary memory, i.e., Random Access Memory (RAM) and Read Only Memory (ROM).

Topic 1.4: Software and Hardware Engineering

• 1.4.1: Differentiate between software engineering and hardware engineering, based on their roles, significance, and applications in computer science.

Topic 1.5: Computer Software

• 1.5.1: Compare system software and application software, highlighting their roles in a computer system.

• 1.5.2: Explain the following types of system software:

  • a. operating system

  • b. device drivers

  • c. utility programs

  • d. language processors

• 1.5.3: Describe the purpose of the following application software:

  • a. word processor

  • b. spreadsheet

  • c. database management

  • d. presentation/desktop publication

  • e. communication

  • f. entertainment

• 1.5.4: Distinguish between open-source, shareware, and freeware software based on their licensing, accessibility, cost, and usage limitations.

Topic 1.6: Programming Languages

• 1.6.1: Describe characteristics, significance, and generation of programming languages.

• 1.6.2: Classify programming languages into low-level (machine and assembly) and high-level (procedural and object-oriented) languages.

• 1.6.3: Describe the following types of language translators:

  • a. compilers

  • b. interpreter

  • c. assembler

Topic 1.7: Data Communication

• 1.7.1: Describe data communication and its components, i.e., sender, message, medium, protocol and receiver.

• 1.7.2: Describe the modes of network communication, i.e., simplex, half duplex and full duplex.

• 1.7.3: Differentiate between the synchronous and asynchronous data transmission methods.

Topic 1.8: Communication Devices

• 1.8.1: Explain the following communication devices:

  • a. hub

  • b. modem

  • c. switch

  • d. router

  • e. gateway

• 1.8.2: Explain structure and functionality of network architecture and its types, including client-server, peer-to-peer, and point-to-point.

Topic 1.9: Computer Networks

• 1.9.1: Explain computer networks and their uses in different fields.

• 1.9.2: Explain different types of computer networks, i.e., Local Area Network (LAN), Wide Area Network (WAN), and Metropolitan Area Network (MAN), highlighting their characteristics and applications.

• 1.9.3: Explain guided media and unguided media.

• 1.9.4: Explain the following network topologies emphasizing their structure, functionality, advantages, and disadvantages:

  • a. bus topology

  • b. ring topology

  • c. tree topology

  • d. star topology

  • e. mesh topology

Topic 1.10: Packet Switching and Circuit Switching

• 1.10.1: Explain packet switching and circuit switching.

Topic 1.11: Data Communication Standards

• 1.11.1: Explain the following data communication protocols highlighting their functions and significance:

  • a. Transmission Control Protocol/ Internet Protocol (TCP/IP)

  • b. Hypertext Transfer Protocol (HTTP)

  • c. File Transfer Protocol (FTP)

Topic 1.12: OSI Model

• 1.12.1: Explain the purpose and functions of OSI model and its following seven layers:

  • a. layer 7 Application layer

  • b. layer 6 Presentation layer

  • c. layer 5 Session layer

  • d. layer 4 Transport layer

  • e. layer 3 Network layer

  • f. layer 2 Data Link layer

  • g. layer 1 Physical layer

Topic 1.13: The Internet

• 1.13.1: Trace the evolution of the internet.

• 1.13.2: Discuss the advantages and disadvantages of the internet, considering its impact on communication, education and society.

Entrepreneurship in the Digital Age

7.1 Exploring Entrepreneurship in the Digital Age

7.1.1 Explain entrepreneurship and its importance in the economy.

7.1.2 Explore famous entrepreneurs and their success stories.

7.2 The Digital Landscape

7.2.1 Explain market trends, consumer needs, and emerging industries.

7.2.2 Apply various idea generation techniques to identify business opportunities.

7.2.3 Analyse the components of a Business Model Canvas (BMC).

7.2.4 Design business models using the Business Model Canvas (BMC).

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7.1 Exploring Entrepreneurship in the Digital Age

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7.1.1 Explain entrepreneurship and its importance in the economy.

✅ What is Entrepreneurship?

Entrepreneurship means starting a new business using your own idea to earn profit.

A person who starts and manages a business is called an entrepreneur.

An entrepreneur:

• Thinks of a new idea 💡

• Takes risk

• Invests money and time

• Tries to earn profit

• Solves people’s problems

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✅ Example of Entrepreneurship

If a student notices that classmates need affordable study notes, and starts selling printed notes at low price, that student becomes an entrepreneur.

If someone creates a mobile app for online tutoring, that is also entrepreneurship.

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✅ Types of Entrepreneurship

1. Small Business Entrepreneurship

   • Small shops

   • Home bakery

   • Tuition center

2. Digital Entrepreneurship

   • Online store

   • YouTube channel

   • Freelancing

   • App development

3. Social Entrepreneurship

   • Business that helps society

   • Example: Company that makes eco-friendly bags to reduce plastic

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✅ Importance of Entrepreneurship in the Economy

Entrepreneurship is very important for a country.

1️⃣ Creates Jobs

When businesses grow, they hire workers.

Example:
A clothing brand hires:

• Designer

• Tailor

• Delivery person

• Social media manager

This reduces unemployment.

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2️⃣ Increases National Income

Businesses produce goods and services.
When people buy them, money moves in the economy.

More businesses = more income = stronger economy.

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3️⃣ Encourages Innovation

Entrepreneurs bring new ideas.

Example:

• Foodpanda changed food delivery.

• Online banking changed payment systems.

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4️⃣ Improves Standard of Living

When people earn more money, they:

• Buy better products

• Get better education

• Live better lives

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5️⃣ Promotes Competition

When many businesses exist, they compete.
Competition improves:

• Quality

• Price

• Customer service

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7.1.2 Explore famous entrepreneurs and their success stories.

✅ What Can We Learn From Entrepreneurs?

Successful entrepreneurs:

• Worked hard

• Faced failure

• Did not give up

• Took risks

• Used technology

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1️⃣ Bill Gates (Microsoft)

• Loved computers from childhood.

• Started Microsoft.

• Made Windows software.

• Helped computers reach homes and offices.

🔹 Lesson: Follow your passion.

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2️⃣ Steve Jobs (Apple)

• Started Apple company.

• Created iPhone, iPad, MacBook.

• Focused on design and innovation.

🔹 Lesson: Think differently.

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3️⃣ Jack Ma (Alibaba)

• Failed many times.

• Rejected from jobs.

• Started Alibaba (online shopping platform).

• Became billionaire.

🔹 Lesson: Failure is part of success.

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4️⃣ Pakistani Example – Airlift / Bykea / Careem

• Used mobile apps.

• Solved transport and delivery problems.

• Used digital technology.

🔹 Lesson: Technology creates new business opportunities.

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7.2 The Digital Landscape

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7.2.1 Explain market trends, consumer needs, and emerging industries.

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✅ 1. Market Trends

Market trend means what is becoming popular in the market over time.

Trends change with:

• Technology

• Fashion

• Customer behavior

Examples:

• Online shopping is increasing.

• Digital payments are replacing cash.

• Healthy food is becoming popular.

Businesses must follow trends to survive.

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✅ 2. Consumer Needs

Consumers are customers.

Consumer needs are:

• What customers want

• What problems they want solved

Types of Consumer Needs:

1. Basic Needs – Food, clothes, shelter

2. Comfort Needs – Fast delivery, easy payment

3. Emotional Needs – Brand trust, good service

👉 Example:
Students need affordable internet packages for online study.

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✅ 3. Emerging Industries

Emerging industries are new and growing industries.

Because of technology, many new industries are growing:

• E-commerce (Daraz)

• Digital marketing

• Freelancing (Fiverr, Upwork)

• Artificial Intelligence

• Online education

• Content creation (YouTube, TikTok)

These industries create many new job opportunities.

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7.2.2 Apply various idea generation techniques to identify business opportunities.

To start a business, we need a good idea.

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✅ 1. Brainstorming

Think of many ideas freely.
Write all ideas without judging.

Example:

• Selling snacks

• Online tutoring

• Graphic designing

• Handmade crafts

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✅ 2. Identify Problems

Every business solves a problem.

Ask:

• What problem do people face?

• How can I solve it?

Example:
Students cannot find affordable notes → Start selling notes online.

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✅ 3. Study Market Trends

Observe what is popular.

Example:
If skincare products are trending → Start organic skincare business.

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✅ 4. Improve Existing Products

Take an old product and make it better.

Example:
Instead of normal water bottles → Sell temperature-control bottles.

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✅ 5. Survey Customers

Ask people:

• What do you need?

• What problems do you face?

Their answers can give business ideas.

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7.2.3 Analyse the components of a Business Model Canvas (BMC).

Business Model Canvas is a planning tool that explains how a business works.

It has 9 building blocks.

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1️⃣ Customer Segments

Who will buy your product?

Example:
Teenagers, working women, students.

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2️⃣ Value Proposition

What special value are you giving?

Example:
Affordable, high-quality school bags.

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3️⃣ Channels

How will you sell?

Example:
Instagram, website, shop.

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4️⃣ Customer Relationships

How will you keep customers happy?

Example:
Fast replies, discounts, loyalty cards.

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5️⃣ Revenue Streams

How will you earn money?

Example:
Selling products, subscription fees.

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6️⃣ Key Resources

What do you need?

Example:
Money, machine, staff, website.

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7️⃣ Key Activities

What work will you do daily?

Example:
Manufacturing, marketing, delivery.

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8️⃣ Key Partners

Who will help you?

Example:
Suppliers, delivery companies.

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9️⃣ Cost Structure

What are your expenses?

Example:
Rent, salaries, electricity.

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7.2.4 Design business models using the Business Model Canvas (BMC).

Let’s design a simple business.

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Example: Online Study Notes Business

Customer Segments:

Grade 9 and 10 students.

Value Proposition:

Affordable, easy-to-understand notes.

Channels:

WhatsApp, Instagram, Google Drive.

Customer Relationship:

Quick response, free sample notes.

Revenue Streams:

Selling PDF notes.

Key Resources:

Laptop, internet, printer.

Key Activities:

Writing notes, marketing, sending PDFs.

Key Partners:

Printing shop, internet provider.

Cost Structure:

Printing cost, internet bill.

Chapter 4: Data and Analysis (Simple Explanation)

 

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4.1 Scope of Data Science

What is Data Science? (Big Picture)

Data Science is all about using data to understand problems, find patterns, and make better decisions.

👉 Example:

• Schools use data to check student results

• YouTube uses data to suggest videos

• Shops use data to know what people like to buy

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4.1.1 Key Concepts of Data Science

a. Data Science

Data Science is a field that collects, studies, and analyzes data to solve real-life problems using computers.

📌 Example:
Netflix studies your watching data to recommend movies.

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b. Data and Dataset

• Data: Raw facts or information

• Dataset: A collection of related data

📌 Example:

• Marks of one student → Data

• Marks of whole class in a table → Dataset

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c. Data Analysis

Data analysis means studying data to find useful information or patterns.

📌 Example:
Teacher checks test results to see which topic students found difficult.

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d. Statistics and Probability

• Statistics: Used to summarize and understand data

• Probability: Used to predict chances of events

📌 Example:

• Average marks = Statistics

• Chance of rain tomorrow = Probability

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e. Mathematics

Mathematics helps in calculations, formulas, and problem-solving in data science.

📌 Example:
Calculating percentages, averages, graphs, etc.

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f. Machine Learning

Machine learning allows computers to learn from data and improve automatically without being programmed again and again.

📌 Example:
Google Maps learns traffic patterns and shows fastest routes.

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g. Deep Learning

Deep learning is a more advanced type of machine learning that works like the human brain.

📌 Example:
Face recognition in mobile phones.

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h. Data Mining

Data mining means finding hidden patterns from large amounts of data.

📌 Example:
Shopping apps find what products are often bought together.

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i. Data Visualization

Data visualization means showing data using charts, graphs, and diagrams so it is easy to understand.

📌 Example:
Pie chart showing favorite subjects of students.

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j. Big Data

Big data refers to very large and complex data that normal computers cannot handle easily.

📌 Example:
Data from Facebook, YouTube, and Google searches.

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k. Predictive Model

A predictive model uses past data to predict future outcomes.

📌 Example:
Predicting exam results based on previous performance.

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l. Natural Language Processing (NLP)

NLP helps computers understand human language.

📌 Example:

• Voice assistants (Siri, Google Assistant)

• Chatbots

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m. Image Processing

Image processing allows computers to analyze and understand images.

📌 Example:

• Medical X-ray analysis

• Camera face detection

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4.1.2 Scope and Applications of Data Science

Data Science is used in many fields:

✔ Education – Student performance analysis
✔ Healthcare – Disease prediction
✔ Business – Customer behavior analysis
✔ Banking – Fraud detection
✔ Sports – Player performance analysis

👉 Scope means data science has a wide future and many job opportunities.

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4.2 Data Types, Data Collection, and Data Storage

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4.2.1 Concept of Data and Its Types

Data

Data is any information that can be recorded.

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Types of Data

1. Qualitative Data

• Descriptive data

• Cannot be measured in numbers

📌 Example:

• Color

• Name

• Opinion

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2. Quantitative Data

• Numerical data

• Can be measured

📌 Example:

• Age

• Height

• Marks

👉 Importance:
Both types help us understand people, behavior, and situations.

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Types of Data (Detailed & Student-Friendly Explanation)

1. Qualitative Data (Data in Words)

Qualitative data is the type of data that describes something.
It tells us “what kind?”, not “how much?”

👉 Very important idea for students:
Qualitative data is about quality, not quantity.

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Why can’t qualitative data be measured in numbers?

Because:

• It does not have size, amount, or value

• It cannot be added, divided, or averaged

Let’s understand with examples 👇

Example 1: Color

Colors tell us what something looks like, not how much it is.

❌ You cannot say:

• Blue = 5

• Red + Green = 10

✔ You can only name or describe colors:

• Red

• Blue

• Black

So, color is qualitative, not numerical.

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Example 2: Name

A name is an identity, not a quantity.

❌ You cannot measure:

• Ali = 10

• Ahmed = 20

✔ You can only identify a person by name.

So, names cannot be measured, only written or spoken.

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Example 3: Opinion

Opinions show feelings or thoughts.

Example:

• “This subject is easy.”

• “I like computer science.”

❌ You cannot measure feelings with a ruler or calculator.

✔ Opinions are expressed in words, not numbers.

👉 That’s why color, name, and opinion are qualitative data.

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Easy Trick for Students to Remember

👉 If data answers “What kind?” or “Which type?” → Qualitative

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2. Quantitative Data (Data in Numbers)

Quantitative data is data that can be counted or measured.

It answers:

• How much?

• How many?

• How tall?

• How old?

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Why quantitative data CAN be measured?

Because:

• It has numbers

• It can be calculated

• It can be compared

Let’s see examples 👇

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Example 1: Age

Age is a number.

✔ You can say:

• 14 years

• 15 years

✔ You can compare:

• 15 is greater than 14

✔ You can calculate:

• Average age of class

So, age is quantitative data.

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Example 2: Height

Height is measured using units.

✔ Examples:

• 150 cm

• 5.5 feet

✔ You can:

• Measure it

• Compare it

• Find tallest student

So, height is quantitative.

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Example 3: Marks

Marks are numbers.

✔ Examples:

• 75 marks

• 90 marks

✔ You can:

• Add marks

• Find percentage

• Calculate average

So, marks are quantitative data.

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Easy Trick for Students to Remember

👉 If data answers “How much?” or “How many?” → Quantitative

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Why Both Types of Data Are Important

Explain this part clearly—it’s exam gold ⭐

• Qualitative data helps us understand qualities and opinions

• Quantitative data helps us measure, compare, and calculate

📌 Real-life example (Classroom):

• Teacher asks students favorite subject → Qualitative

• Teacher checks marks in exam → Quantitative

📌 Another example (School):

• School uniform color → Qualitative

• Number of students → Quantitative

👉 Both together give complete information.

4.2.2 Data Collection Process

Data collection means gathering data from different sources.

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a. Websites

Data is collected from online platforms.

📌 Example:
Online surveys, social media data.

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b. Sensors

Sensors collect data automatically.

📌 Example:

• Temperature sensors

• Speed sensors

• Smart watches

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c. Surveys

Questions are asked to collect opinions.

📌 Example:
School feedback forms.

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Ethical Considerations

✔ Take permission
✔ Do not misuse data
✔ Protect privacy

📌 Example:
Apps should not steal personal information.

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4.3 Big Data and Applications of Big Data in Business

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What is Big Data? (Student Language)

Big Data means a very large amount of data that is:

• too big,

• too fast,

• and too different

👉 It cannot be handled easily by a normal computer.

📌 Simple example for students:
One student’s marks = normal data
Marks of millions of students across countries = Big Data

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Why is it called “BIG” Data?

Because this data:

• comes from many sources

• comes every second

• is in different forms

Example sources:

• Mobile phones

• Social media

• Online shopping

• Sensors

• Government records

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3 Vs of Big Data (MOST IMPORTANT CONCEPT)

Explain this slowly. Tell students:
👉 Big Data is big because of 3 reasons (3 Vs)

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1. Volume (Amount of Data)

Volume means how much data there is.

📌 Example:

• One photo = small data

• Millions of photos uploaded on Instagram daily = big volume

👉 Normal computers cannot store such huge data easily.

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2. Velocity (Speed of Data)

Velocity means how fast data is generated.

📌 Example:

• WhatsApp messages sent every second

• Live cricket scores updating instantly

👉 Data is coming very fast, so systems must work quickly.

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3. Variety (Different Types of Data)

Variety means different forms of data.

📌 Examples:

• Text (messages, posts)

• Images (photos)

• Videos

• Audio

• Numbers (marks, prices)

👉 All these together make data complex.

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Easy Memory Trick for Students

👉 Big Data = Volume + Velocity + Variety

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What is Big Data Analytics?

Big Data Analytics means:

Studying big data to find useful information and make better decisions.

📌 Example:

• Amazon studies what you search and buy

• Then suggests products you may like

👉 Without analytics, big data is useless.

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Data Visualization and Interpretation

Data Visualization

Showing data using:

• Graphs

• Charts

• Diagrams

📌 Example:
Sales shown in a bar graph.

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Data Interpretation

Understanding what the data is telling us.

📌 Example:
Graph shows sales increased → Business is growing.

👉 Visualization helps us see,
👉 Interpretation helps us understand.

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Big Data Challenges in Business

Explain this as “problems businesses face”.

1. Data Security

Risk of data theft or hacking.

📌 Example:
Customer bank details stolen.

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2. Privacy Issues

Using data without permission is wrong.

📌 Example:
Selling user data without consent.

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3. High Cost

Storing and processing big data is expensive.

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4. Data Management

Handling too much data is difficult.

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Applications of Big Data in Business

Now students see WHY businesses use it.

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a. Healthcare

Big data helps in:

• Disease prediction

• Patient records

• Medical research

📌 Example:
Hospitals analyze patient data to predict diseases early.

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b. Internet of Things (IoT)

IoT devices collect data using sensors.

📌 Examples:

• Smart watches

• Smart homes

• Traffic sensors

👉 Data is used to improve comfort and safety.

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c. Manufacturing

Big data helps factories:

• Detect machine problems early

• Improve product quality

📌 Example:
Sensors warn before machine breakdown.

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d. Government

Big data helps government:

• Manage traffic

• Plan cities

• Control crime

• Conduct census

📌 Example:
Smart city systems.

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Why Big Data Is Important (Student Logic)

Explain clearly:

• Small data → Simple decisions

• Big data → Smart and accurate decisions

👉 Businesses that use big data:
✔ Understand customers
✔ Reduce loss
✔ Increase profit

4.3.1 Big Data Concepts

a. Big Data

Big data is huge data generated every second from different sources.

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b. 3 Vs of Big Data

1. Volume – Large amount of data

2. Velocity – Speed of data generation

3. Variety – Different types of data (text, images, videos)

📌 Example:
Social media posts.

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c. Big Data Analytics

Big data analytics means analyzing big data to make smart decisions.

📌 Example:
Amazon suggests products based on your searches.

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d. Data Visualization and Interpretation

• Visualization = graphs and charts

• Interpretation = understanding what data shows

📌 Example:
Sales chart shows profit increase.

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4.3.2 Big Data Challenges in Business

❌ Data security
❌ High cost
❌ Privacy issues
❌ Data management

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4.3.3 Applications of Big Data in Business

a. Healthcare

• Disease prediction

• Patient monitoring

📌 Example:
Tracking patient health records.

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b. Internet of Things (IoT)

Devices connected to internet collect data.

📌 Example:
Smart homes, smart watches.

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c. Manufacturing

• Quality control

• Predict machine failure

📌 Example:
Factories use sensors to avoid breakdowns.

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d. Government

• Traffic control

• Crime analysis

• Census data

📌 Example:
Smart city systems.

Unit 4: Data and Analysis-SLO

 

4.1 Scope of Data Science

• 4.1.1 Explain the following key concepts of data science:

  • a. data science

  • b. data and dataset

  • c. data analysis

  • d. statistics and probability

  • e. mathematics

  • f. machine learning

  • g. deep learning

  • h. data mining

  • i. data visualization

  • j. big data

  • k. predictive model

  • l. natural language processing (NLP)

  • m. image processing

• 4.1.2 Discuss the scope and application of data science.

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4.2 Data Types, Data Collection, and Data Storage

Student Learning Outcomes Students should be able to:

• 4.2.1 Explain the concept of data and its types (qualitative and quantitative) emphasizing their characteristics and importance.

• 4.2.2 Evaluate the process of data collection using websites, sensors, and surveys, highlighting its significance and ethical considerations through real-world examples.

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4.3 Big Data and Applications of Big Data in Real World Business

Student Learning Outcomes Students should be able to:

• 4.3.1 Describe the following concepts of big data within the context of technology and society:

  • a. big data

  • b. 3 Vs of big data

  • c. big data analytics

  • d. data visualization and interpretation

• 4.3.2 Describe big data challenges in business.

• 4.3.3 Explain the application of big data in the following business domains:

• a. healthcare

• b. internet of things (IoT)

• c. manufacturing

• d. government

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