Data Literacy Class 9 Questions and Answers are designed to help students understand the fundamental concepts of data and its importance in artificial intelligence. This chapter covers topics such as data collection, data types, data interpretation, and the role of data in making informed decisions.
Data Literacy Class 9 Questions and Answers
1. What is data literacy?
Answer: Data literacy is the ability to understand, explore, and communicate with data. The data-literate people can derive meaningful information from data. Data literacy helps to analyze complex data or big data in summarized form.
2. What is the data pyramid?
Answer: The data pyramid is a method to transform the raw data to meaningful information, which helps to understand the data deeper and helps to make better decisions. The data pyramid is also known as the DIKW pyramid, where “D” means data, “I” means information, “K” means knowledge, and “W” means wisdom.
3. What is the difference between qualitative and quantitative data?
Answer:
| 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 textual data | Any measurements, readings, or values would count as numeric data |
| Example: “Which is a good park nearby?” | Example: Cricket Score, Restaurant Bill |
4. What is cybersecurity?
Answer: Cybersecurity means protecting computers, phones, and networks from hackers or viruses. This protection can be done using antivirus, strong passwords, and safe browsing.
5. How can someone become data literate?
Answer: To become data literate, first we have to understand the data and practice reading charts and tables. We can also use simple tools like Excel or Google Sheets for analyzing data.
6. Why is cybersecurity important?
Answer: Cybersecurity is important because it helps to keep personal information safe. It helps to protect against loss or fraud from hackers. Cybersecurity helps to protect privacy and trust.
7. What are data security and privacy?
Answer: When you protect data using passwords, encryption, and firewalls, it means you are making the data secure. Data privacy are the rules about how the personal data or information is collected or used.
8. What are the do’s and dont’s in cybersecurity?
Answer: The do’s and don’ts in cybersecurity are –
Do’s
- 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, OS, and antivirus updated regularly.
Dont’s
- Avoid sharing personal info like 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 info like bank account details.
- Keep passwords and security questions private.
- Don’t copy copyrighted software without permission.
- Avoid cyberbullying or using offensive language online.
9. What is data and what are its types?
Answer: The data are raw facts; these data are divided into two types: quantitative data (numbers) and qualitative data (words, categories).
10. What are the different sources of data?
Answer: There are two methods used to acquire data from the various sources.
- Primary Data Source – If the data is collected from the original source, it is known as a primary data source. For example, surveys, experiments, etc.
- Secondary data source – This type of data is collected from external sources and is known as a secondary data source. For example, books, reports, databases, etc.
11. What are the different types of data interpretation?
Answer: Data interpretation is the process of making sense out of data that has been processed; the interpretation of data helps us answer critical questions using data.
The type of data interpretation data—
- Textural Data Interpretation: In this data is mentioned in the form of text, usually in a paragraph.
- Tabular Data Interpretation: This data interpretation is represented in the form of rows and columns.
- Graphical Data Interpretation: This data interpretation represents the data in the form of graphs like bar graphs, pie charts, and line charts.
12. What is data acquisition?
Answer: Data acquisition is a process where real-world data can be collected and converted into a digital form with the help of computers and software. For example, if you want to predict the salary of any employee based on the previous salaries, then you have to acquire the data from the different reliable sources, or you can feed the data directly to the machine.
13. What are the ethical concerns in data acquisition?
Answer: While gathering data and choosing datasets, certain ethical issues can be addressed before they occur.
- Bias: Takes steps to understand and avoid any preferences or partiality in data.
- Consent: Take necessary permissions before collecting or using an individual’s data.
- Transparency: Explain how you intend to use the collected data and do not hide intentions.
- Anonymity: Protect the identity of the person who is the source of data.
- Accountability: Take responsibility for your actions in case of misuse of data.
14. What are the features of data and data processing?
Answer:
The feature of data is
- Structure: How data is stored (tables, files, databases).
- Cleanliness: Data should be free from duplicates, missing values, and errors.
- Accuracy: Data must match real‑world values correctly.
- Characteristics: Each piece of data has properties (e.g., student’s name, age, grade).
- Independent Features: Inputs used to make predictions (like study hours).
- Dependent Features: Outputs we want to predict (like exam scores).
The feature of data processing is
- Collection: Gathering data from surveys, sensors, or online sources.
- Cleaning: Removing duplicates, fixing missing values, correcting errors.
- Organization: Arranging data into tables or categories.
- Transformation: Converting raw data into usable formats.
- Analysis: Applying statistics or models to find patterns.
- Storage: Saving data securely for future use.
- Presentation: Showing results in charts, graphs, or reports.
15. What are the different methods of data interpretation?
Answer: Data interpretation is the process of making sense out of data that has been processed; the interpretation of data helps us answer critical questions using data.
There are two ways to interpret data—
- Qualitative Data Interpretation – The qualitative data used for text data for interpretation; qualitative data tells us about the emotions, thoughts, experiences, and feelings of the people.
- Quantitative Data Interpretation – Quantitative data interpretation is a process for analyzing numerical data using statistical methods.
16. What are the steps of qualitative data analysis?
Answer: There are five steps to qualitative data analysis—
- Collect Data
- Organize
- Set a code for the data collected
- Analyze your data
- Reporting
17. Make a checklist of factors that make data good or bad.
Answer: The checklist of factors that make data good or bad are
Good Data
- Information should be well structured.
- Data should be accurate.
- Data should be consistent.
- Data should be cleanly presented.
- The data contain information that is relevant to our requirement.
Bad Data
- Information is scattered.
- Contain a lot of incorrect value
- Contains missing and duplicate values
- It is poorly presented.
- It contains information that is not relevant to our requirement.
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