a) Utilize vocabulary and analytical skills b) Acquire, develop, and improve data literacy skills c) Develop skills in statistical methodologies d) Develop skills in Math
Q5. _______________________ is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.
a) data security b) data literacy c) data privacy d) data acquisition
Ans. Data visualization is important because in this we create visuals like charts and graphs which helps us to understand the complex data. It also help us to identify patterns, trends from raw data.
Ans. Data privacy referred to proper handling of sensitive data including personal data and other confidential data such as certain financial data and intellectual property data.
Q4. What are the best practices that can help us to ensure data privacy?
Ans. The following best practices can help us to ensure data privacy:
● Understanding what data, we have collected, how it is handled, and where it is stored. ● Necessary data required for a project should only be collected. ● User consent while data collection must be of utmost importance.
Q5. What do you mean by Data Security. Why it is important?
Ans.Data Acquisition, also known as acquiring data, refers to the procedure of gathering data. This involves searching for datasets suitable for training AI models.
Data Acquisition typically comprises three key steps:
1. Data Discovery: In this step we search for new datasets.
2. Data Augmentation: In this step we generate a new data by adding more data to the existing data.
3. Data Generation: In this step we generate new data if data is not available.
Unit 2 Data Literacy Class 9 AI Question Answers
Q11. What are the factors that make data good or bad?
Ans. The two sources for Acquiring Data in AI models are:
Primary Data Sources — Some of the sources for primary data include surveys, interviews, experiments, etc. The data generated from the experiment is an example of primary data.
Secondary Data Sources—Secondary data collection obtains information from external sources, rather than generating it personally. Some sources for secondary data collection include Kaggle, , UCI etc
Ans. The three primary factors that determine the usability of data are:
1. Structure: It defines how data is stored. Data stored in spreadsheet is more structured rather than data stored in Text document.
2. Cleanliness: Clean data is free from duplicates, missing values, outliers, and other anomalies that may affect its reliability and usefulness for analysis
3. Accuracy: Accuracy indicates how well the data matches real-world values, ensuring reliability. Accurate data closely reflects actual values without errors, enhancing the quality and trustworthiness of the dataset.
Unit 2 Data Literacy Class 9 AI Question Answers
Q16. What do you mean by Data features? Explain with example.
Ans. Data features are the characteristics or properties of the data. They describe each piece of information in a dataset. For example, in a table of student records, features could include things like the student’s name, age, or grade.
Q17. Differentiate between the two types of Data features in AI models.
Ans. Data processing refers to operating on data to produce meaningful information. Data processing helps computers to understand raw data.
Data Interpretation refers to analyzing data to arrive at meaningful decisions. It is the process of making sense out of data that has been processed. It helps us to answer critical questions using data.
Q20. Name and explain two types of Data Interpretation.
1. Qualitative Data Interpretation: Qualitative data tells us about the emotions and feelings of people. It is focused on insights and motivations of people.
2. Quantitative Data Interpretation: Quantitative data interpretation is made on numerical data. It helps us answer questions like “when,” “how many,” and “how often”. For example – (how many) numbers of likes on the Instagram post.
Unit 2 Data Literacy Class 9 AI Question Answers
Q21. Write all the five Steps to Qualitative Data Analysis.
1. Textual Data Interpretation: In this data is mentioned in the text form, usually in a paragraph. It is used when the data is not large and can be easily comprehended by reading. It is not suitable for large data.
2. Tabular Data Interpretation: Data is represented systematically in the form of rows and columns. It is easy to compare values and find pattern.
3. Graphical Data Interpretation: Visual tools like bar charts, pie charts, line graphs, histograms are used in this to communicate data.
Q27. Name the software which make it easier for us to present data.
