JAI GURU DEV

CLASS 9 DATA LITERACY

SOLVED QUESTIONS
A. Choose the correct option
1. Which of the following is not an example of data?
a. Audio C. Video
b. Text d. Hardware
2. The ______ illustrates the progressive transformation of
raw data into actionable wisdom
a. Data C. Data literacy
b. Data Pyramid d. Information
3. The data pyramid begins with_____
a. Top level C. Raw Data
b. Information d. Knowledge
4. Data literacy enhances ______ability in individuals based
on evidence.
a. Programming C. Understanding
b. Decision making d. Information
5. Designing an ______ metric for the data literacy program
involves creating a structured framework
a. Mathematical C. Logical
b. Skill d. Evaluation
6. ________is about hunting for valuable information in
different places, checking if it’s good quality, and making
sense of what we find.
a. Data discovery C. Data science
b. Data quality d. Data literacy
7. They can recognize objects, people, and even actions
happening in videos
a. NLP C. Data Science
b. Data d. Computer Vision
8. Which of the following is not the method of data
collection in qualitative data interpretation?
a. Record keeping C. Observation
b. Case studies d. Driving
 9. _______should be organized in a way that makes sense
so that it can be used effectively.
a. Data C. Knowledge
b. Privacy d. Ethics
10. __________ should not have duplicates, missing
values, outliers, and other anomalies so that its reliability
and usefulness for analysis is not affected
a. Text Data C. Clean data
b. Visual data d. Ethical
B. Fill in the blanks
1. Data refers to any collection of raw facts, figures,
statistics, or information that can be stored and
processed by a computer.
2. Data literate is a person who can interact with data to
understand the world around them.
3. Data literacy can equip individuals with skills and
knowledge to improvise in a data driven world.
4. The data literacy data literacy framework provides a
comprehensive and structured approach to develop the
necessary skills for using data efficiently with all levels of
awareness.
5. Data augmentation means increasing the amount of
data by adding copies of exisiting data with small
changes.
6. The data generated from the experiment is an example
of primary data
7. Tableau is a powerful data visualization and business
intelligence tool
8. In Tabular DI, data is represented systematically in the
form of rows and columns.
9. Quantitative data interpretation is made on numeric
data
10. In longitudinal studies data collection method ,
data is collected on the same data source repeatedly
over an extended
period of time.
C. State whether these statements are true or false.
1. Data literacy framework is an iterative process. True
2. Data analysis is used to examine each component of
the data in order to draw conclusions. True
3. Qualitative data tells us about the numbers of the
data. False
4. Data from people's experience is a data collection
method in quantitative data. False
5. In a pie graph, data is represented using vertical and
horizontal bars. False
6. Data interpretation helps in making informed
decisions by providing a clearer picture of the
situation. True
7. Descriptive statistics are not used in quantitative data
analysis. False
8. Reporting is one of the steps involved in qualitative
data analysis. True
Short answer
1. From the given image answer the following
questions

A B C D E
In the given image typical levels of awareness in
a data literacy process of framework are shown
Identify the Labels marked as A, B ,C, D, E ,F
Ans:
A- Plan
B- communicate
C -Assess
D-Develop culture
E- Prescriptive learning
F- Evaluate
2. What is prescribed learning?
Ans. By implementing a prescriptive learning
approach, organisations can provide a set of diverse
resources that align with individual
learning styles.
3. What is Data Acquisition? Give an example
 Ans. Data acquisition, also known as acquiring
data, refers to the procedure of gathering data like raw
facts, figures or statistics from
relevant sources either for reference or for
analysis as needed in AI projects. For example: collection
of data to predict the topper of
the school in the upcoming board
examination.
4. Name the Sources of Data Acquisition
Ans. Sources for acquiring data can be Primary
or Secondary.
5. Why is it important for data to be clean?
Ans. Clean data is free from duplicates,
missing values, and anomalies, ensuring its reliability and
usefulness for analysis.
6. What role does Tableau play in data
presentation?
Ans. Tableau is a powerful tool for visualising
and analysing data, aiding in business decisions by
creating various charts, graphs, maps,
and dashboards.
7. Why is it important to choose the
appropriate measurement scale for data
analysis?
Ans. Choosing the correct scale ensures
accurate measurement and representation of data, leading to
valid analysis results.
8. Describe a scenario where identifying
needs through data interpretation can lead
to reduced costs.
Ans. A restaurant owner could use customer
feedback data to modify or remove unpopular dishes from
the menu, reducing waste and
costs.
9. What are some common features of bar
graphs and line graphs?
 Ans. Both graph types represent data visually,
with bar graphs using bars and line graphs using points
connected by lines to show
changes over time.
10. Give an example of a situation where
record keeping would be a useful data
collection method.
Ans. Using library documents as reliable and
curated sources of information for data collection

Long answer
1. Give the impacts of data literacy in Education and
business?
Ans. Data Literacy has an immense impact on various
aspects of society like business, education, healthcare, and
public policy, some of them are
given here:
 Business: It improves the decision making
skills of a person. Data-literate employees can
effectively analyse data to gain insights into
market trends, customer behaviours, and
operational performance.
 Education: It empowers the teaching-
learning process. Students can engage more
deeply with course material, particularly in
STEM fields.
2. List down the ways that will help you to become
data literate.
Ans. Here is a guide to help you become data literate:
 Understand the Basics: Start from learning the
concepts of data, types of data and how it can be used.
 Learn Data Analysis Tools: There are many data
analysis apps available that can be learned in order to
understand the impact of right data.
 Gain Statistical Knowledge: Statistics play a vital role

in data literacy. Its one of the vital components that

must be learned before you dive into the data driven
world.
  Use Data Visualisation: Understand the techniques of
data visualisation such as Graphics and Charts. Tools
like Tableau, matplotlib, python can be used effectively
for this purpose.
 Learn Data Manipulation: Understanding how to
manipulate data to meet the requirements is also one of
the key factors. Methods like filtering,
sorting, grouping and omitting are essential for
extracting insights from large data set.
 practise Cleaning' Learning to remove data redundancy
and data inaccuracy is essential to be data literate.
3. What are the steps involved in Data Acquisition?
Ans. The three key steps involved in Data Acquisition are
as follows:
1. Data discovery is about hunting for valuable
information in different places, checking if its good
quality, and making sense of what we find.

2. Data augmentation It is the process of increasing

the amount and diversity of data. We do not collect
new data, rather we transform the already present
data. Data augmentation means increasing the
amount of data by adding copies of existing data with
small changes. The image given here does not
change, but we get data on the image by changing
different parameters like colour, rotation, flipping
and brightness. New data is added by slightly
changing the existing data.

3. Data generation It refers to generating or

recording data using sensors. Recording
temperature readings of a building is an example
 of data generation. Recorded data is stored in a
computer in a suitable form.

4. Explain the importance of having a clear structure in

data and provide examples of good and poor data
structure.
Ans. Clear structure in data ensures it is organised
logically, facilitating efficient analysis and
interpretation. For example, marks of students
arranged in a spreadsheet is a good structure,
whereas a poor structure example if the student
records were stored in a disorganised manner, with
inconsistent naming conventions or missing attributes,
it would impede data analysis and decision-making
processes.

5. Why is Data Privacy important?

Ans. It is important because:
 A data breach at a government agency can put top
secret information in the hands of an enemy state.
 A breach at a hospital can put personal health
information in the hands of those who might misuse
it.
 A breach at a corporation can put proprietary data
in the hands of a competitor.
 A breach at a school can cause inconvenience to
the parents, such as constant tuition and coaching
centers keep calling.

6. Explain the term Computer Vision and the type

of data used in this ?
 Ans. Computer Vision is like giving eyes to computers. It
helps them look at pictures and videos from the real
world and understand what they're seeing. With
Computer Vision, computers can figure out what's in a
picture or video, just like we do. They can recognise
objects, people, and even actions happening in videos.

Types of data used in Computer Vision include:

 Image Data: Digital images captured by cameras

or satellite imagery, and medical scans.
 Video Data: Video data captured using camera,
and surveillance footage.
c. Competency based /application based questions
1. Your teacher has asked students to give the choice of
atleast 3 co-curricular activities from the given list
a. Painting f. Dance-Indian
b. Music-western g. Best of waste
c. Music-Indian h. English
theatre
d. Dance-Western i. Hindi theatre
You are provided with a dataset containing errors, duplicates,
and missing values. How would you approach organising and
cleaning this data to ensure its reliability and usefulness for
analysis?
Outline the steps you would take to organise and clean dataset,
ensuring that is free from errors, duplicates, and missing values.
Additionally, describe any methods or techniques you would use
to address these issues and ensures the dataset’s reliability and
usefulness for analysis.
Ans:
Organising and cleaning the dataset containing co-curricular
activities involves several steps to ensure it is free from errors,
duplicates, and missing values, thus making it reliable and useful
for analysis.
Initial Assessment: Review dataset structure and identify
errors, duplicates and missing values.
Standardisation: Normalise activity names and correct spelling
inconsistencies
Duplicate removal: Identify and remove exact duplicates of
activities
Missing values: Impute missing data using appropriate
methods like mode for categorical value.
validation: Cross-verify cleaned data against original sources
for accuracy.
Quality Assurance: Ensure data consistency and document
cleaning steps for transparency.
Export: save cleaned dataset in a suitable format (e.g CSV) for
analysis
Final check: Validate data integrity post-export to ensure
reliability for analysis.
2. Assertion and reasoning questions
Assertion: (A): Cleaning data is a crucial step in the
data analysis process.
Reasoning: Data cleaning ensures that the dataset is
free from errors, duplicates , and missing values
,which enhances the reliability and accuracy of
subsequent analysis.
a. Both A and R are true and R is the correct
explanation of A
b. Both A and R are true and R is not the correct
explanation of A
c. A is true but R is false
d. A is false but R is true
Unsolved questions
Section A
1. Data literacy is able to cultivate _____skills to understand
and explore data’s implications by questioning
assumptions
a. Critical thinking C. Programming
b. awareness d. Probability
2. Data literacy fuels ______ by providing tools and
techniques to explore data from different perspectives
a. Errors c. Comprehension
b. Innovation d. Repetition
3. _____ enables users to tackle complex problems and
derive meaningful relevance
a. Mathematics c. Trends
 b. Project cycle d. Data literacy
4. Data literacy has an impact on which of the following?
a. Public policy C. Cooking
b. Driving d. Jogging
5. By implementing a ______learning approach,
organisations can provide a set of diverse resources that
align with individual learning styles
a. Modern C. prescriptive
c. planned d. latest
6. The process of collecting data from websites using
software is called_____
a. Data analysis C. Data reference
c. Data literacy d. Data scraping
7. ______ is the process of increasing the amount and
diversity of data.
a. Data augmentation C. Data filtering
b. Data processing d. Data modelling
8. Digital images captured by cameras or satellite imagery,
medical scans, and surveillance footage is______
a. Text data C. Numeric data
c. Computer vision d. Audio data
9. _______is the process of making sense out of a collection
of data that has been processed.
a. Data interpretation C. Data scrapping
c. Data validation d. Data handling
10. The __________ is the first step of data handling
a. Plan C. Acquire
b. present d. Process
b. Fill in the blanks
1. Data literacy refers to the ability to read, comprehend
and use information effectively
2. Data literacy is essential because it enables individuals
to make decisions, think critically, solve problems and
innovate’
3. Data pyramid is a conceptual model that illustrates the
hierarchical structure of data processing
4. Secondary data sources are the external sources for
collecting data rather than generating it personally
 5. Data generation refers to generating or recording data
using sensors
6. NLP is all about teaching computers to understand and
work with human language
7. Data features are also called the characteristic or
properties if the data
8. Application involves putting acquired knowledge and skills
into practice in real-world contexts
9. Strong password is a combination of upper and
lowercase letters, numbers and special characters that are
difficult for unauthorized individuals or automated
programs to guess or crack
10. Data backup refers to the process of creating copies
of data to ensure that it can be restored in the event of
data loss due to natural disasters, accidents, cyber-
attacks, or other unexpected events.
C. State whether these statements are true or false.
1. Data analysis is a common method for extracting information
from websites. False
2. Recording temperature readings of a building is an example of
data generation. True
3. Data augmentation is the process of increasing the amount
and diversity of data. True
4. Secondary data includes data taken from surveys, interviews,
experiments, etc., False
5. Good data has information scattered. False
6. Highest level of understanding involves critical thinking to
interpret data and make good consistent decisions. True
7. Accurate data closely reflects actual values with errors. False
8. Data interpretation involves collecting the data. False
D. Match the following
1. computer vision Data scraping
2. NLP Image data
3. Textual data Dataset search
4. Sources of data Audio data(speech)
5. Data discovery Qualitative data