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These Class 9 Introduction to Data Literacy Notes that help you understanding key concepts, improving accuracy, and boosting your final exam score. This notes of Data Literacy Class 11 AI are desinged as per latest CBSE Curriculum.

Basics of Data Literacy

Data Literacy

Data Literacy means the ability to:

• understand data
• work with data
• communicate (talk about) data
• and use data to make decisions

The Data Pyramid

• Data Pyramid is made of different stages of working with data.
• Data becomes more useful as we move upward in stages.
• Different parts of Data Pyramid are (moving from bottom):
  • Data
  • Information
  • Knowledge
  • Wisdom

Data

• Unprocessed facts and figures which is not very meaningful on their own
• Example: numbers, words, ratings without context

Information

• Data that is organized or processed and make sense
• Example: grouping or ordering reviews of movies

Knowledge

• Understanding patterns, relationship, or conclusions from information
• Helps us know what is happening

Wisdom

• Using knowledge, experience, and understanding to make smart decisions
• Helps us understand why something is happening and what action should be taken

Data Pyramid Example:

Impact of Data Literacy

• Helps in better decision-making
• Improves critical thinking
• Helps in problem-solving
• Supports innovation

How to Become Data Literate

• Understand that every data tells a story, but it should be analysed before trusting
• Learn to interact with data to understand real-world situations
• Develop the habit of using data while making decisions (not guessing)

Example:

• While shopping online, practice identifying:
  • Cheapest product using price comparison
  • Most liked product using user ratings and reviews
  • Suitable product by checking required features
• Use filters effectively based on your needs
  • Example: Apply “price low to high” for budget shopping
• Check user ratings and feedback to understand product quality
• Verify whether the product meets all your specific requirements
• Compare multiple options instead of selecting the first result
• Build the habit of making decisions based on data, not advertisements

How a Data Literate Person Thinks:

A data literate person will:

• Use filters smartly
  • Example: Sort price from low to high if the budget is limited
• Analyze user ratings and reviews
  • Check average rating and read feedback from users
• Match requirements with features
  • Compare product specifications with personal needs before buying

Data Literacy Framework

A Data Literacy Framework is a step-by-step process to plan, teach, improve, and measure data skills in learners.

Plan

• Set the goal of the program
• Understand who the learners are
• Decide how and when it will be done

Communicate

• Explain why the program is important
• Share goals clearly with learners
• Get their support and involvement

Assess

• Check current data skills of learners
• Understand how comfortable they are with data
• Use simple tests or tools for evaluation

Develop Culture

• Encourage regular use of data in learning
• Make data-based thinking a habit
• Build a learning environment that supports data use

Prescriptive Learning

• Provide different learning resources
• Let learners choose what suits them best
• Support learning based on individual needs

Evaluate

• Check progress regularly
• Measure improvement in skills
• Improve the program based on results

What are Data Security and Data Privacy? How are they related to AI?

Data Privacy

Data Privacy (also called information privacy) means controlling how personal and sensitive data is collected, used, shared, and stored.

It ensures that:

• Only necessary data is collected
• Data is used only for the purpose it was collected
• User permission (consent) is taken before collecting data
• Sensitive data (like bank details, personal identity, etc.) is protected from misuse

Why Data Privacy is Important

• Protects personal identity and sensitive information
• Prevents misuse of user data
• Builds trust between users and companies
• Helps follow legal rules and regulations

Best Practices for Data Privacy

• Collect only required data
• Take user consent before collecting data
• Clearly explain how data will be used
• Store data securely and limit access

Data Security

Data Security means protecting digital data from unauthorized access, theft, damage, or corruption.

It focuses on technical protection methods like:

• Password protection
• Encryption
• Firewalls
• Secure servers

Example:
When your email account is protected with a password and OTP verification, that is data security.

Why Data Security is Important

• Cyberattacks and hacking are increasing
• Cloud storage increases risk of data leaks
• Protects data from theft or damage
• Ensures safe data transfer over networks

Difference Between Data Privacy and Data Security

Data Privacy	Data Security
Focuses on how data is collected and used	Focuses on how data is protected
Concerned with rules and consent	Concerned with tools and protection methods
Example: user permission	Example: encryption, passwords

Best Practices for Cyber Security

Cyber security involves protecting computers, servers, mobile devices, electronic systems, networks, and data from harmful attacks.

Do’s (Good Practices for Cyber Security)

• Use strong, unique passwords with a mix of characters for each account.
• Activate Two-Factor Authentication (2FA) for added security.
• Download software from trusted sources and scan files before opening.
• Prioritize websites with “https://” for secure logins.
• Keep your browser, operating system, and antivirus updated regularly.
• Adjust social media privacy settings to limit visibility to close contacts.
• Always lock your device screen when away.
• Connect only with trusted individuals online.
• Use secure Wi-Fi networks.
• Report online bullying to a trusted adult immediately.

Don’ts (Things to Avoid)

• Do not share personal information like real name, address, or phone number.
• Do not send pictures to strangers or post sensitive photos on social media.
• Do not open emails or attachments from unknown sources.
• Ignore suspicious requests for personal information like bank account or OTP details.
• Keep passwords and security questions private.
• Do not copy or use copyrighted software without permission.
• Avoid cyberbullying or using offensive language online.

Acquiring Data, Processing, and Interpreting Data

Types of Data

Artificial Intelligence depends on data as its foundation. In everyday life, we deal with different kinds of information, which can be classified into various types of data.

Textual Data (Qualitative Data)

• Textual data is made up of words and phrases.

• It is used for Natural Language Processing (NLP)
• It helps machines understand human language
• Search queries on the internet are an example of textual data
• Example: “What is your favourite color?”

Numeric Data (Quantitative Data)

• Numeric data is made up of numbers.

• It is used for statistical analysis
• It includes measurements, readings, or values
• It is widely used in calculations and data processing
• Examples: No of Books, Restaurant bill, Height of Student

Types of Numeric Data

Continuous Data

• Continuous data is numeric data that can take any value within a range, including decimals.
• Examples: Height, Weight, Temperature, Voltage

Discrete Data

• Discrete data is numeric data that contains only whole numbers and cannot be fractional.
• Examples: Number of students in a class, Number of books in a bag, Number of goals in a match

Types of Data used in three domains of AI:

Data Acquisition / Acquiring Data

Data Acquisition (also called acquiring data) refers to the process of collecting data. It involves finding and gathering datasets that can be used to train AI models.

The process mainly includes three steps:

Data Discovery

• Data discovery means finding and collecting existing data from sources like the internet or databases.
• Example: To build a self-driving car model, we collect images of roads, traffic signals, and vehicles from online datasets.

Data Augmentation

• Data augmentation means increasing the size of a dataset by creating modified versions of existing data.

• The original data is slightly changed to create new data
• Changes can include brightness, color, rotation, or flipping in images
• Example: An image of a road can be modified by changing brightness or contrast to create more training data.

Data Generation

• Data generation means creating or recording new data using sensors or devices.
• Example: Recording temperature readings of a building using sensors and storing them in a computer system.

Sources of Data

Various sources are used for acquiring data:

Primary Data Sources

• Primary data sources refer to data that is collected directly from original sources.
• It is collected through: Surveys, Interviews, Experiments
• Example: Data collected from students of a class through a survey or experiment is primary data.

Secondary Data Sources

• Secondary data sources refer to data that is collected from existing external sources, rather than being collected personally.
• It includes data obtained from: Websites, Books, Research reports, Government records, Online databases
• Example: Using internet datasets or published reports to analyze student performance is secondary data.

Best Practices for Acquiring Data Checklist of factors that make data good or bad

Data acquisition from websites

Ethical concerns in data acquisition

Features of Data and Data Preprocessing

The Three Primary Factors of Data Usability are:

• Structure
• Cleanliness
• Accuracy

Structure

• Defines how data is stored.

Cleanliness

• Clean data is free from duplicates, missing values, outliers, and other anomalies that may affect its reliability and usefulness for analysis.

Accuracy

• Accuracy indicates how well the data matches real-world values, ensuring reliability.
• Accurate data closely reflects actual values without errors, which directly enhances the quality and trustworthiness of the dataset.

Platform Evaluation of Data Usability

Kaggle Usability Scores: Platforms like Kaggle assign a specific usability score to the datasets hosted on their website. This score is determined based on ratings and feedback given by the actual users of that data.

Data Features

Data features are the specific characteristics or properties of your data. They break down and describe each individual piece of information within a dataset.

Depending on the type of data you are working with, features can vary:

• Tabular Data (e.g., Student Records): Features include concrete data points like a student’s name, age, or final grade.
• Visual Data (e.g., Photo Dataset): Features might include more abstract details, such as the specific colors or pixel patterns present in each image.

The Two Types of Features in AI Models

When training and building Artificial Intelligence models, data features are categorized into two main functional roles:

Independent features: They are the input to the model—The information we provide to make predictions.

Dependent features: They are the outputs or results of the model—they’re what we’re trying to predict.

Analogy: If you are building an AI model to predict a student’s final exam score, the hours spent studying and attendance rate are the independent features (inputs). The actual exam score predicted by the model is the dependent feature (the output you want to discover).

Data Processing and Data Interpretation

Data Processing

• Data processing helps computers understand raw data.
• It involves using computers to perform different operations on data.

Data Interpretation

• Data interpretation is the process of making sense of processed data.
• It helps in answering important questions using data.

Understanding Some Keywords Related to Data

• Acquire Data: Acquiring data means collecting data from various sources.
• Data Processing: After raw data is collected, it is processed to derive meaningful information from it.
• Data Analysis: Data analysis means examining each part of the data to draw conclusions.
• Data Interpretation: Data interpretation means explaining what the findings or conclusions mean in a given context.
• Data Presentation: Data presentation means selecting, organizing, and presenting data or ideas in a logical and clear way.

Methods of Data Interpretation

Based on the two types of data, there are two ways to interpret data:

• Quantitative Data Interpretation
• Qualitative Data Interpretation

Qualitative Data Interpretation

Qualitative data tells us about the feelings, emotions, and experiences of people. It focuses on understanding insights and motivations.

Methods of Collecting Qualitative Data

• Record Keeping: Using existing documents and reliable sources of information.
• Observation: Observing behavior and emotions of participants.
• Case Studies: Collecting detailed information from specific cases.
• Focus Groups: Group discussions on a topic to gather opinions.
• Longitudinal Studies: Studying the same source over a long period of time.
• One-to-One Interviews: Direct interviews with individuals.

5 Steps of Qualitative Data Analysis

1. Collect data – Gather relevant information from different sources.
2. Organize data – Arrange the collected data in a proper structure.
3. Code the data – Assign labels or codes to simplify analysis.
4. Analyze data – Examine the data to find patterns or conclusions.
5. Report findings – Present the results in a clear and meaningful way.

Quantitative Data Interpretation

Quantitative data interpretation is based on numerical data. It helps answer questions like how many, when, and how often.

Example: Number of likes on a social media post.

Methods of Collecting Quantitative Data

• Interviews: Quantitative interviews are important for collecting numerical data from individuals.
• Polls: A poll is a type of survey that asks simple, often single-question responses from people.
• Observations: Quantitative data is collected by observing and recording data over a specific time period.
• Longitudinal Studies: These are studies conducted over a long period of time to track changes in data.
• Surveys: Surveys are used to collect quantitative data from a large number of people using structured questions.

Steps of Quantitative Data Analysis

1. Relate measurement scales with variables
2. Connect descriptive statistics with data
3. Decide measurement scale
4. Represent data in appropriate format

Qualitative vs Quantitative Data Interpretation

Qualitative Data Interpretation	Quantitative Data Interpretation
Categorical	Numerical
Feelings and emotions	Quantity and numbers
How and why	When, how many, how often
Interviews, Focus Groups	Assessments, Tests, Polls, Surveys
Why do students like attending online classes?	How many students like attending online classes?

Types of Data Presentation (Data Interpretation)

Textual Data Interpretation

• Data is written in paragraph form
• Suitable for small data
• Not suitable for large datasets

Tabular Data Interpretation

• Data is represented in rows and columns
• Organized and easy to compare

Graphical Data Interpretation

• Data is shown using visuals

Types:

• Bar Graph: Data shown using bars
• Pie Chart: Data shown in circular slices
• Line Graph: Data points connected to show change over time

Importance of Data Interpretation

• Identify needs of people
• Helps convert raw data into meaningful information.
• Supports better decision-making based on facts.
• Reduce cost by making efficient decisions.
• Helps identify patterns and trends in data.
• Makes it easier to understand large and complex data.
• Helps in predicting future outcomes using past data.
• Improves accuracy in analysis and conclusions.

Project Interactive Data Dashboard & Presentation

Data Visualization Using Tableau

• Download Tableau public with the help of an adult using this link – https://public.tableau.com/en-us/s/download
• Install the package via the install wizard.
• Once installed, double click the program to open the Tableau Public Desktop application.
• Now pull in data. (make sure you entered all data in excel sheet)
• To pull data click on Microsoft Excel in the top left corner
• Now drag the sheet with your data to Drag tables here section.
• Now to create Bar chart click on Sheet1 in the bottom left corner of the screen
• Hover over the word “Genre”. You will notice a blue oval appear behind it.
• Click and drag “Genre” up and to the right, releasing it next to the word Columns when a little orange arrow appears.
• Hover over the word “Genre”. You will notice a blue oval appear behind it.
• Click and drag “Genre” up and to the right, releasing it next to the word Columns when a little orange arrow appears.
• Now drag “Sample (Count)” to Rows, following the same steps as above.
• “Sample (Count)” represents the total number of songs in your table.
• Tableau made us a bar graph!

Make each bar a different color

• Simply click and drag “Genre” out to where it says Color.
• Tableau colored our genres for us!

Make the text a little more fun and easier to read

click the label square.

This opens up a box that allows us to change the font and text size.

Let’s change the font size and font as per your choice.