Data Literacy Data Collection to Data Analysis Class 11 Questions and Answers

These Class 11 AI Important Questions Chapter 5 Data Literacy Data Collection to Data Analysis Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Data Literacy Data Collection to Data Analysis Class 11 Important Questions

Class 11 Data Literacy Data Collection to Data Analysis Important Questions

Important Questions of Data Literacy Data Collection to Data Analysis Class 11 – Class 11 Data Literacy Data Collection to Data Analysis Important Questions

Data Literacy Data Collection to Data Analysis Class 11 Very Short Answer Type Questions

Question 1.
Explain the concept of data literacy and its importance in today’s digital age.
Answer:
Data literacy refers to the ability to find, understand, and use data effectively. In today’s digital age, where data is abundant, data literacy is crucial for making informed decisions, understanding trends, and solving complex problems. It helps individuals and organizations extract valuable insights from data to drive innovation and growth.

Question 2.
What is data preprocessing?
Answer:
Data preprocessing is the process of cleaning and preparing raw data before it is used for analysis or modelling. It involves handling missing data and outliers to ensure the data is accurate and reliable.

Question 3.
How does a line graph differ from a bar graph?
Answer:
A line graph is used to show trends over time with continuous data, while a bar graph is used to compare different categories of data with discrete values.

Question 4.
When would you use a scatter plot?
Answer:
A scatter plot is used to show the relationship between two variables in a set of paired data, helping to identify correlations or trends between the variables.

Question 5.
Name four elements that, when combined, help a tale be interesting?
Answer:

• Involve your audience in the narrative
• Build tension for the conclusion
• Make characters come to life
• The conclusion of the climax is anticipated to be favorable.

Question 6.
What is data literacy?
Answer:
Data literacy refers to the ability to effectively find, understand, evaluate, and use data.

Question 7.
Why is data diversity important in data collection for AI models?
Answer:
Data diversity helps AI models cover more scenarios, improving their reliability and applicability.

Question 8.
What are the levels of measurement in data exploration?
Answer:
The levels of measurement include nominal, ordinal, interval, and ratio scales, each offering different properties for data analysis.

Question 9.
How does statistics contribute to understanding data in AI projects?
Answer:
Statistics, through measures like mean, median, mode, and standard deviation, helps summarize and interpret data for AI decision-making.

Data Literacy Data Collection to Data Analysis Class 11 Short Answer Type Questions

Question 1.
What is data visualization and why is it important?
Answer:
Data visualization is the graphical representation of data to help people understand the significance of data by summarizing and presenting it in a visual form such as charts, graphs, or maps. Data visualization is important because it allows for the exploration and understanding of data patterns, trends, and outliers that may not be apparent in raw data. It helps in making data-driven decisions and communicating information clearly and efficiently.

Question 2.
What is data?
Answer:
Data can be defined as a representation of facts or instructions about some entity (students, school, sports, business, animals etc.) that can be processed or communicated by human or machines.

Question 3.
What do you mean by web scraping?
Answer:
Web scraping is the process of using bots to extract content and data from a website. Web scraping extracts underlying HTML code and, with it, data stored in a database. The scraper can then replicate entire website content elsewhere.

Question 4.
If a matrix has 6 elements, what are the possible orders it can have?
Answer:
4 orders – (1 × 6), (6 × 1), (2 × 3) and (3 × 2)

Question 5.
Construct a 3 × 2 matrix where each element is given by aij = i * j
Answer:
3 × 2 matrix means 6 elements a11 = 1 × 1 a12 = 1 × 2 a 21 = 2 × 1 a22 =2 × 2 a 31 = 3 × 1 a 32 = 3 × 2
Putting all elements in matrix form we get: A = [1 2 2 4 2 3 6]

Question 6.
Find the transpose of the matrix B = [5-1 4 2 3]
Answer:
B = [5 2 -1 3 4 6]

Data Literacy Data Collection to Data Analysis Class 11 Long Answer Type Questions

Question 1.
Discuss the advantages and limitations of using a pie chart in data visualization. Provide examples to illustrate your points.
Answer:
One advantage of using a pie chart is that it can effectively show the proportion of each category in a dataset. For example, a pie chart can be used to visualize the market share of different companies in a specific industry. However, pie charts have limitations, such as difficulty in comparing multiple datasets or showing trends over time. For example, a pie chart would not be suitable for visualizing changes in sales over the course of a year, as it cannot effectively convey this type of information.

Question 2
What is conflict and resolution?
Answer:
Conflict can be defined as a clash between two opposing forces that creates the narrative thread for a story. It is the most exciting and engaging drive in any story.
The following skills can be used to resolve conflict in data storytelling:

• Teamwork
• Problem solving
• Strest management
• Communication
• Emotional flexibility

Question 3.
What do you mean when you talk about a story’s setting and plot?
Answer:
The story’s plot is what takes place. A story’s plot centres on a single event (a pattern break, a turning point) that poses a dramatic question that needs to be resolved as the narrative develops. Location and time are what make up the setting of a story. It might be made up or genuine.

Question 4.
Explain the terms mean, median and mode.
Answer:

Question 5.
Explain the four levels of measurement.
Answer:
Four levels of measurements are Nominal, Ordinal, Interval and Ratio The nominal level of measurement is the simplest or lowest of the four ways to characterize data. Nominal means “in name only”. Colours of eyes, yes or no responses to a survey, gender, smartphone companies, etc all deal with the nominal level of measurement. Ordinal data, is made up of groups and categories which follow a strict order.

For e.g. if you have been asked to rate a meal at a restaurant and the options are: unpalatable, unappetizing, just okay, tasty, and delicious. The options is Ordinal and the data is qualitative,. Like the nominal scale data, ordinal scale data cannot be used in calculations. Data that is measured using the interval scale is similar to ordinal level data because it has a definite ordering but in interval scale data does not have a starting point i.e. zero value.

Temperature scales like Celsius (oC) and Fahrenheit (F) are measured by using the interval scale. Ratio scale data is like interval scale data, but it has a o point and ratios can be calculated. We can add, subtract, divide and multiply the two ratio level variables. Eg: Weight of a person. It has a real zero point. Hence it can be considered as ratio value.

Question 6.
Given the matrices A and B. Calculate A+ B and B – A.

Answer:

Question 7.
The ages of a group of people in a community are: 25,28,30,35,40,45,50,55,60,65 : Write a program to calculate the mean, median, and mode of the ages.
Answer:

Question 8.
A company recorded the daily temperatures (in degrees Celsius) for five consecutive days: 20° C, 22° C, 25° C, 18° C, and 23° C. Determine the variance and standard deviation of the temperatures.
Answer:

Question 9.
Plot a line chart representing the weekly number of customer inquiries received by a customer service center:
Week 1: 150 inquiries
Week 2: 170 inquiries
Week 3: 180 inquiries
Week 4: 200 inquiries

Question 10.
Plot a bar chart representing the number of books sold by different genres in a bookstore:
Fiction: 120 books
Mystery: 90 books
Science Fiction: 80 books
Romance: 110 books
Biography: 70 books

Question 11.
Visualize the distribution of different types of transportation used by commuters in a city using a pie chart:
Car: 40%
Public Transit: 30%
Walking: 20%
Bicycle: 10%

Case-Based Questions

Question 1.
In AI class, Sona heard about the importance of data story in an AI project but she is still having some doubts. Help her in understanding the importance of data story.
Answer:
Like a story, data story also help in engaging the audience across the boundary and culture. The data point holds some information that may be unclear and contextually deficient on its own. Thus it is visualization in the form of statistics, numbers reduces ambiguity, connects data with context and describes a specific interpretation.

Question 2.
Mohan joined a new school as a teacher. He found that the students are weak in some subjects but they are not ready to listen him. So he thought to present their overall performance in the form of graph. Gradually after seeing the graph their performance increased. What do you think what is the reason behind this?

Answer:
The information presented in the graph by Mohan, created a good data story setting, i.e. a strong narrative around the data, making it is easier to understand the pre and post data, existing problem, action taken by Mohan and the resolution of the problem.

Question 3.
Anakha is working on a project involving mood analysis of individuals experiencing depression. She plans to visit hospitals, yoga instructors, mental health care providers, and healthcare institutes. How can Anakha collect data from these organizations and individuals for her project?
Answer:
Surveys, Interviews, Questionnaire

Question 4.
Kalaimathi and her friend are planning to build an admission chatbot for her school. They have collected data from different sources with the help of their friends. Now, they need to arrange it in proper order to analyse it. They plan to classify the data based on the levels of measurement. The data they have collected is as follows: Student Name, Age, Gender, GPA (Grade Point Average), ECA (Extra-Curricular activities), Place, Parent Name, Parent Education level, Distance from school, Fees, Interview rating, No. of years in last school, Admission Test score
Answer:

Question 5.
During a sales analysis, metrics such as sales revenue per month, average sales revenue, and the most popular products sold are examined to comprehend overall performance and aid in decision-making regarding marketing strategies, inventory management, and resource allocation. Which measurements in statistics will facilitate the sales analysis process?
Answer:
Mean, Median Mode

Question 6.
Selvan’s Textiles operates multiple showrooms in a city. Fahad is working on a project to predict the annual sales percentage for the upcoming year. He intends to analyse the trend of sales percentage over time and also the sales percentages in different regions. To facilitate data analysis, he plans to visualize the data using graphs.
a. Which type of graph would be most appropriate for visualizing the trend of sales percentage over time?
b. Which type of graph would be most suitable for comparing sales percentages across different regions?
Answer:
a. Line Chart
b. Pie Chart

Question 7.
Akshith wrote a program to visualize the data analysis of five test and marks got.

The program did not have any errors. But the line graph was not showing up. Could you find the reason why the graph is not shown even though the program has no errors?
Answer:
In order to view the graph we have to use show( ) function in the program. If we write pl.show( ), the graph window will be displayed.

Scenario Based Question Answer:

Scenario: Emily is a data scientist tasked with developing an AI model to predict customer, preferences for a new product launch. She needs to ensure her team understands the fundamentals of data literacy and effective data collection methods. During a team meeting, she decides to clarify key concepts through scenario-based questions.

Question 1.
Question: Emily explains the importance of data literacy in AI projects. She asks her team, “Why is data literacy crucial for leveraging artificial intelligence effectively?”
Answer:
Data literacy is crucial because AI relies on converting large amounts of raw data into actionable insights. Without understanding how to find, evaluate, and use data effectively, AI models cannot generate meaningful outcomes. Data literacy ensures that data is collected ethically, organized correctly, and analyzed accurately to derive insights that support decisionmaking processes in AI applications.

Question 2.
Question: Emily discusses the challenges and considerations in data collection. She asks, “What factors should we consider when deciding how much data to collect for our AI model?”
Answer:
When deciding how much data to collect, several factors should be considered:

• Model Complexity: More complex AI models, such as those used in medical AI, typically require larger volumes of data to ensure accuracy and reliability.
• Data Diversity: Collecting diverse data ensures the model covers various scenarios and improves its robustness.
• Application Scope: Understanding where and how the AI model will be used helps determine the types and amounts of data needed.
• Iterative Process: Data collection is an iterative process that involves understanding the problem, defining data requirements, identifying data sources (primary and secondary), and continuously refining the dataset based on model performance.

Data Literacy Data Collection to Data Analysis Class 11 Notes

Data Literacy:

• Definition: Data literacy refers to the ability to effectively find, understand, evaluate, and use data.
• Skills: Includes collecting, organizing, checking data quality, analyzing, interpreting results, and using data ethically.
• Importance: Essential for leveraging AI and making informed decisions based on actionable insights from data.

Data Collection:

• Definition: Gathering data from various sources for analysis.
• Methods: Includes primary (surveys, interviews, observations, experiments) and secondary (social media, web scraping, satellite data) sources.
• Considerations: Importance of data diversity and quantity, depending on the complexity and application of AI models.

Exploring Data:

• Purpose: Understanding data characteristics (typical, unusual, spread) to identify and correct issues that could affect analysis outcomes.
• Levels of Measurement: Classifies data into nominal, ordinal, interval, and ratio scales based on properties like order and mathematical operations.

Statistical Analysis of Data:

• Central Tendency: Measures (mean, median, mode) used to summarize the central value of a dataset.
• Python Libraries: Utilizes statistical functions like mean, median, mode, variance, and standard deviation for analysis.

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