DATA LITERACY(IX)

Q1: What is Data Literacy?

Ans: Data literacy means knowing how to understand, work with, and talk about data. It's about being
able to collect, analyze, and present data in meaningful ways.

Q2: Explain the different parts of the Data Pyramid.

Ans: The Data Pyramid consists of:

• Data::Data is available in a raw form. Raw data that is not very useful in its initial form.
• Information: Processed data that provides insights about the world.
• Knowledge: Information that helps us understand how things are happening.
• Wisdom: Allows us to understand why things are happening in a particular way.

Q3: Who is Data Literate?

Ans: A data literate person can interact with data to understand the world around them. Data literacy
helps individuals research products while shopping online.

Q4: How does data literacy help in online shopping?

Ans: Data literacy helps individuals:

• Filter categories according to requirements, such as setting a price filter from low to high if on a
budget.
• Check user ratings of products.
• Verify specific requirements for the product.

Q5: Describe the Data Literacy Process Framework.

Ans: The data literacy framework provides guidance on using data efficiently and involves an iterative
process of collecting, analyzing, and interpreting data.
Q6: What are Data Security and Privacy? How are they related to AI?

Ans: Data Privacy and Data Security are related but distinct concepts:

• Data Privacy: Refers to the proper handling of sensitive and confidential data, including personal
and financial information, to meet regulatory requirements and protect data confidentiality.
• Data Security: The practice of protecting digital information from unauthorized access, corruption,
or theft throughout its entire lifecycle.

Q7: Why is data security important?

Ans: Data security is crucial due to the increasing amount of data stored in the cloud, which heightens
the risk of cyber threats. It is essential to control and protect sensitive or personal information.

Q8: Give examples of actions that can compromise data privacy.

Ans:

• Downloading an unverified mobile application.

• Accepting Terms of Service without reading them.
• A data breach at a government agency.
 • A breach at a corporation exposing proprietary data to competitors.
• A breach at a hospital exposing personal health information.

Q9: What is Cyber Security?

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

Q10: List some best practices for cyber security.

Ans:

• 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 for limited visibility to close contacts.
• Always lock your screen when away.
• Connect only with trusted individuals online.
• Use secure Wi-Fi networks.
• Report online bullying to a trusted adult immediately.

Q11: What should you avoid doing to maintain cyber security?

Ans:

• Avoid sharing personal information such as real name or phone number.

• Don’t send pictures to strangers or post them on social media.
• Don’t open emails or attachments from unknown sources.
• Ignore suspicious requests for personal information, like bank account details.
• Keep passwords and security questions private.
• Don’t copy copyrighted software without permission.
• Avoid cyberbullying or using offensive language online.

Q12: What are the main types of data used in artificial intelligence, and how are they classified?

Ans: In Artificial Intelligence, data is broadly classified into two main types:

Textual Data (Qualitative Data) Numeric Data (Quantitative Data)

● It is made up of words and phrases ● It is made up of numbers

● It is used for Natural Language Processing (NLP) ● It is used for Statistical Data
● Search queries on the internet are an example of ● Any measurements, readings, or values
textual data would count as numeric data
● Example: “Which is a good park nearby?” ● Example: Cricket Score, Restaurant Bill
Numeric Data is further classified as:

• Continuous Data: Numeric data that can take any value within a range (e.g., height, weight,
temperature, voltage).
• Discrete Data: Numeric data that contains only whole numbers and cannot be fractional (e.g.,
number of students in a class).

Q13: Explain the process of data acquisition in AI and its key components.

• Ans: Data Acquisition, also known as acquiring data, refers to the procedure of gathering data for
training AI models. It typically comprises three key steps:

• Data Discovery: Finding suitable datasets, such as images of roads and objects for training a self-
driving car.
• Data Augmentation: Increasing the dataset size by slightly modifying existing data, like adjusting
colors or brightness in images.
• Data Generation: Creating new data through sensors or other means, such as recording
temperature readings of a building.

Q14: What are the sources of data?

Ans:

• Primary Data Sources: Includes surveys, interviews, experiments, etc. Example: Data collected from
experiments or direct observations.
• Secondary Data Sources: Obtains information from external sources rather than generating it
personally. Examples: Published research, external databases.

Q15: Differentiate between good data and bad data.

Ans:

• Good Data: Accurate, complete, and relevant data that meets the requirements of its intended use.
• Bad Data: Inaccurate, incomplete, outdated, or irrelevant data that can lead to misleading
conclusions or errors.
Q16: What are the primary factors that determine the usability of data in AI applications?

Ans: The three primary factors are:

• Structure: Defines how data is stored and organized, impacting its accessibility and processing
efficiency.
• Cleanliness: The absence of duplicates, missing values, outliers, and errors, ensuring reliability for
accurate analysis and model training.
• Accuracy: How well the data matches real-world values, ensuring reliability and enhancing the
quality and trustworthiness of the dataset.

Q17: What are data features, and how do they help us understand and analyze data?

Ans: Data features are the characteristics or properties of data that describe each piece of information
in a dataset. For example:

• In a student records table, features could include the student’s name, age, or grade.
• In a photo dataset, features might represent the colors present in each image.
These features help us comprehend and analyze the data effectively.

Q18: Explain the difference between independent and dependent features in AI models.

Ans:

• Independent Features: Serve as inputs to the model, used to make predictions.

• Dependent Features: The outputs or results of the model, representing what we aim to predict.

Q19: Why is data processing important, and how does it help us make sense of processed data?

Ans: Data processing is crucial because it allows us to transform and organize raw data into meaningful
information. It helps answer critical questions and derive insights by structuring data for better analysis
and interpretation.

Q20: What are the methods of data interpretation?

Ans: There are two primary methods of data interpretation:

• Quantitative Data Interpretation

• Qualitative Data Interpretation

Q21: What does qualitative data interpretation focus on?

Ans: Qualitative data interpretation focuses on understanding the emotions, feelings, insights, and
motivations of people.
 Q22: What are the data collection methods associated with qualitative data interpretation?

Ans: Methods include:

• Record Keeping: Using existing reliable documents and similar sources.

• Observation: Analyzing participants’ behavior and emotions.
• Case Studies: Collecting data from detailed investigations of individual or group cases.
• Focus Groups: Gathering data from group discussions on relevant topics.
• Longitudinal Studies: Collecting data from the same source repeatedly over an extended period.
• One-to-One Interviews: Conducting in-depth interviews with individuals.

Q23:Ethical concerns in data acquisition

While gathering data and choosing datasets, certain ethical issues can be addressed before they occur

Q24: What are the methods of data interpretation?

There are two ways to interpret data based on its type:
• Quantitative Data Interpretation
• Qualitative Data Interpretation

Q25: What does qualitative data interpretation focus on?

 Qualitative data interpretation centers around understanding the emotions, feelings,
insights, and motivations of people.

Q26: What are the data collection methods associated with qualitative data
interpretation?

Two methods are commonly used:

• Record keeping: This involves using existing reliable documents and similar sources of
information as the data source.
• Observation: Participants’ behavior and emotions are observed and analyzed.

Q27: What are the two types of features in AI models?

Answer: The two types of features in AI models are independent features and dependent features.
Independent features are the inputs provided to the model to make predictions, while dependent
features are the outputs or results that the model aims to predict.

Q28. What is the importance of data processing and data interpretation?

Answer: Data processing and interpretation are crucial because they help us make sense of the
data that has been processed. Interpretation allows us to answer critical questions and derive
meaningful insights from the data.

Q29: What are the two methods of data interpretation mentioned?

Answer: The two methods of data interpretation mentioned are Quantitative Data Interpretation
and Qualitative Data Interpretation.

Q30: What is the focus of qualitative data interpretation?

Answer: Qualitative data interpretation focuses on understanding the emotions, feelings, insights,
and motivations of people.

Q31. What are some methods of data collection for qualitative data interpretation?

Answer: Some methods of data collection for qualitative data interpretation include:

• Record keeping
• Observation
• Case studies
• Focus groups
• Longitudinal studies
• One-to-one interviews
 Q32: What is quantitative data interpretation and what types of questions does it help answer?

Answer: Quantitative data interpretation involves analyzing numerical data to answer questions
such as "when," "how many," and "how often." For example, it can determine the number of likes
on an Instagram post.

Q33: What are some methods of data collection for quantitative data interpretation?

Answer: Methods of data collection for quantitative data interpretation include:

• Interviews: Quantitative interviews gather numerical data through structured questions.

• Polls: Simple surveys with limited questions, often just one question.
• Observations: Collecting numerical data through observations over a specific time period.
• Longitudinal Studies: Studies conducted over an extended period to track changes over time.
• Surveys: Questionnaires administered to a large number of people to collect quantitative data.

Q34: What are the three ways in which data can be presented?

Answer: The three ways to present data are:

• Textual Data Interpretation (DI): Data is presented in paragraph form, suitable for small
amounts of data that can be easily read and understood.
• Tabular Data Interpretation (DI): Data is arranged in rows and columns, often used for
systematic representation and detailed information.
• Graphical Data Interpretation (DI): Data is presented using visual formats such as bar graphs,
pie charts, and line graphs to show trends and proportions.

Q35: What are the characteristics of textual data interpretation?

Answer: Textual data interpretation involves presenting data in paragraph form. It is suitable for
small datasets that can be easily understood through reading. It is not ideal for large amounts of
data.

Q36: How is data represented in tabular data interpretation?

Answer: In tabular data interpretation, data is systematically organized in rows and columns. The
table includes a title describing the content and column headings that detail the information in each
column.

37: Describe the different types of graphical data interpretation.

Answer: The different types of graphical data interpretation are:

• Bar Graphs: Represent data with vertical or horizontal bars, showing quantities for different
categories.
• Pie Charts: Circular charts divided into slices, with each slice representing a proportion of the
whole.
• Line Graphs: Display data points connected by lines, illustrating changes in quantity over time.

Q38: What is quantitative data interpretation and what types of questions does it help answer?

Answer: Quantitative data interpretation involves analyzing numerical data to answer questions such
as "when," "how many," and "how often." For example, it can determine the number of likes on an
Instagram post.

Q39 What are some methods of data collection for quantitative data interpretation?

Answer: Methods of data collection for quantitative data interpretation include:

• Interviews: Quantitative interviews gather numerical data through structured questions.

• Polls: Simple surveys with limited questions, often just one question.
• Observations: Collecting numerical data through observations over a specific time period.
• Longitudinal Studies: Studies conducted over an extended period to track changes over time.
• Surveys: Questionnaires administered to a large number of people to collect quantitative data.

Q40. What are the three ways in which data can be presented?

Answer: The three ways to present data are:

• Textual Data Interpretation (DI): Data is presented in paragraph form, suitable for small amounts
of data that can be easily read and understood.
• Tabular Data Interpretation (DI): Data is arranged in rows and columns, often used for systematic
representation and detailed information.
• Graphical Data Interpretation (DI): Data is presented using visual formats such as bar graphs, pie
charts, and line graphs to show trends and proportions.

Q:41. What are the characteristics of textual data interpretation?

 Answer: Textual data interpretation involves presenting data in paragraph form. It is suitable for small
datasets that can be easily understood through reading. It is not ideal for large amounts of data.

Q42: How is data represented in a tabular data interpretation?

Answer: In tabular data interpretation, data is systematically organized in rows and columns. The
table includes a title describing the content and column headings that detail the information in each
column.

QE43. Describe the different types of graphical data interpretation.

Answer: The different types of graphical data interpretation are:

• Bar Graphs: Represent data with vertical or horizontal bars, showing quantities for different
categories.
• Pie Charts: Circular charts divided into slices, with each slice representing a proportion of the
whole.
• Line Graphs: Display data points connected by lines, illustrating changes in quantity over time.
Tabular DI Title of the Table (Item of Expenditure) contains the description of the table content.
Column Headings (Year; Salary; Fuel and Transport; Bonus; Interest on Loans; Taxes) contains the description
of information contained in columns. ▪ ▪ Each section of the pie chart is proportional to the corresponding
value Line Graphs It shows the change in quantity over time.