Data Literacy

 

Part B Unit II

DATA LITERACY

A.      Tick the correct option:

 1.      Which of the following is not a type of data literacy?

         (a)               Textual                                                (b) Tabular

         (c)                Graphical                                            (d) Raw data
 
2.      ________ relates to the manipulation of data to produce meaningful insights.

         (a) Data Processing                                         (b) Data Interpretation

         (c) Data Analysis                                           (d) Data Presentation
 
3.      Which of the following best defines data literacy?

 

(a)   The ability to read and write data codes

(b)   The ability to understand and use data effectively

(c)    The ability to memorize large databases

(d)   The ability to create databases

 

4.      Which of the following is a key principle of data literacy?

 

(a)   Data should be shared freely with everyone

(b)   Data should be kept indefinitely

(c)    Data should be collected and used transparently

(d)   Data should never be used

 

5.      What is a primary risk associated with poor data?

(a)   Increase data redundancy

(b)   Higher data processing speeds

(c)    Data breaches and loss of sensitive information

(d)   Decreased data storage costs

 

6.      What is the first step in data interpretation?

(a)   Collecting data

(b)   Analysing data

(c)    Cleaning data

(d)   Visualising data

 

7.       When is a pie chart most appropriately used?

(a)   To show trends over time

(b)   To compare proportions within a whole

(c)    To show the relationship between two variables

(d)   To display data distribution

 

8.      What is the primary purpose of a dashboard in data visualisation?

(a)               To create complex data models

(b)               To provide an attractive and comprehensive view of key metrics

(c)                To store large datasets

(d)               To write and execute data queriess

B.  Fill in the blanks:

a.      Data literacy involves the ability to understand, interpret, and effectively use Data make informed decisions.

 

b.      A Dataset is a collection of data points organised in a structured format, typically rows and columns.
 
 
c.        In data analysis, a research question is a specific question or issue that data is collected to address.
 
d.      A Pie chart is useful for displaying the proportions of a whole as slices of a circle.
 
e.      Data visualization is the process of representing data in a visual context to make it easier to understand and interpret.
 
f.        Interactive features like filters allow user to filter and explore different aspects of the data in a dashboard.
 
g.       A line chart is ideal for showing changes over time, with data point collected by a continuous line.
 
h.      A bar chart is a graphical representation of data where individual’s values are represented by the height or length of bars.
 
C. State whether the following True or False:

a.       Data that has been processed, organized, or structured to provide context and meaning is information. True
b.      Data security basically governs how data is collected, shared and used. False
c.       Data should be stored and transmitted securely to protect it from unauthorized access, disclosure, or misuse. True
d.      The information extracted through data science can be used to make a decision about it. True
e.      Data literacy is only important for data scientists and IT professionals. False
f.        Data visualization helps in understanding complex data by representing it graphically. True
g.      Using too many colours in a single chart can make it harder to understand. True
h.      Interactive dashboards allows users to explore different aspects of the data. True

 

D. Assertion and Reasons Questions:

Given below are two statements in each question. One is labelled as Assertion(A) and the other is labelled as Reason(R). Consider both the statements and choose the correct answer.

i.         Both A and R are correct and R is the correct explanation of A

ii.  Both A and r are correct but R is not correct reason of A

iii.  A is correct but R is not correct

iv.  A is not correct but R is correct

 

a.      Assertion(A): The objective functions in business organizations are complex requiring the processing of large volume of data.

 Reason(R): AI based non-linear modals can solve the problems or arriving at the dynamic solution to the business problems.

Ans. ii

 

b.      Assertion(A): AI systems require large datasets to make accurate predictions and decisions.

Reason(R):  More data allows Ai system to identify pattern sand trends, improving their accuracy.

Ans. i

 

c.       Assertion(A): Data privacy is an essential component of AI data literacy.

Reason(R):Protecting personal information within datasets is not required as long as the AI provide accurate results.

Ans. iii

 

d.      Assertion(A): Data literacy involves understanding the sources, quality and ethical use of data in AI.

     Reason(R): Knowing how to process and interpret data is not rasa important as the amount of data available to an AI system.

Ans. iii

e.      Assertion(A): An AI modal trained on the biased data may produce unfair outcome.

 Reason(R): Biased data leads to AI algorithmslearning incorrect associations that can       disadvantage certain groups.

Ans. i

 

E. Answer the following questions:

1. Who is a data literate? How can a person become data literate?

Ans. A person who is not only able to read, analyses and understand data but also can draw meaningful conclusions, and make informed decisions based on data is a data literate. To become data literate, a person must develop the skills to read, work with, analyze, and communicate using information effectively. One must start from understanding statistics and learning interpret basic charts, tables, and metrics. Next, individuals must learn to work with data by cleaning messy datasets, and recognizing data types. Finally, the process is completed by mastering data storytelling, which allows a person to translate complex numbers into clear narratives that drive confident, fact-based decisions.

Q2. What are the steps involved in data literacy framework process?

Ans. The following are the steps in data literacy framework process.

(i)        Plan: At first stage the purpose of the data analysis process and specific goals are identified.          Apart  from this, volume of data, available resources, data source and tools are determined.

(ii)      Communicate: Communicate the importance of data literacy to all stakeholders.

(iii)    Assess: Track progress  towards achieving data literacy goals and objectives.

(iv)     Develop Culture: Cultivate a data-driven culture where data literacy is valued and integrated into everyday practices.

(v)       Prescriptive Learning:  Develop customized learning paths from employees based on their roles, skill levels, and learning preferences.

(vi) Evaluate: Evaluate the impact of data literacy initiative on organizational performance and outcomes.

Q3. Differentiate between data privacy and data security.

Ans.

Aspect	Data Privacy	Data Security
Definition	Data privacy is all about the reflection of what data is important and why.	Data security is all about the reflection of how those policies got enforced.
Focus	Data privacy sets about proper usage, collection, retention, deletion, and storage of data.	Policies, procedures, and tools for protecting personal data are established by data security.
Prerequisite	Data security gives prerequisite to data privacy.	Data security is the main prerequisite to data privacy.
Purpose	It offers to block websites, internet browsers, and internet service providers from tracking your information and your browser history.	It offers to protect you from other people accessing your personal information and other data.
Data protection	Data privacy basically governs how data is collected, shared and used.	Data security basically protects data from compromise by external attackers and malicious insiders

 

Q4. Mention the do’s and don’ts in ensuring cyber security

Ans. Do’s

·         Regularly update operating system, applications and security software.

·         Create complex passwords.

·         Perform regular backups of important data.

·         Exercise caution when clicking on links or downloading attachment from unsolicited sources.

Don’ts

·         Avoid using common and easily guessable passwords.

·         Avoid clicking on lines from unknown or suspicious sources.

·         Avoid disabling security features such as firewalls, antivirus software etc.

·         Avoid connecting to unsecured public Wi-Fi networks.

·         Take cyber security alerts and warning seriously.

Q5. What are the ethical concerns in data acquisition?

Ans. The following are the some of the ethical concern in data acquisition.

a. Privacy and Confidentiality: Implement strong security measures, anonymise data where possible, and obtain informed consent from individuals.

b. Bias and Fairness: Use diverse datasets, employ bias detection and mitigation techniques, and involve diverse stakeholders in data collection and analysis.

c. Informed Consent: Clearly communicate purposes of data collection, provide opt-out options, and ensure consent is freely given without undue influence.

d. Transparency and Accountability: Publish clear privacy policies, disclose data usage practices, conduct regular audits of data handling practices.

e. Data Quality and Integrity: Implement rigorous data validation and cleaning processes, document data sources and methodologies.

Q6.  Explain data processing and data interpretation.

Ans.

Data processing: Data processing is the method of collecting raw data and transforming it into a structured format suitable for analysis. This include handling missing values, correcting errors, standardizing formats and integrating data from multiple sources so that organizations can use it to make decisions.

Data Interpretation: Data interpretation is the process of reviewing organized data to arrive at a meaningful conclusion. It involves in translating analytical findings into actionable insights.

Data interpretation is the process It involves taking processed and visualized data (like charts, tables, or graphs), analyzing it, and using it to make informed decisions or answer specific questions.

 

Q7. Write the data collection methods used in qualitative data interpretation.

Ans. Data collection methods in case of qualitative data interpretation are as under:

Record Keeping: This method uses existing reliable documents and other similar sources of information as the data source. It is similar to going to a library.

Observation: In this method, the participant- their behaviour and emotions - are observed carefully.

Case Studies: In this method, data is collected from case studies.

Focus Groups: In this method, data is collected from a group discussion on relevant topic.

Longitudinal Studies: This data collection method is performed on the same data source repeatedly over an extended period.

One-to-One Interviews: In this method, data is collected using a one-to-one interview.

Q8. Describe the ways in which data can be presented.

Ans. Data can be presented in several ways depending on the complexity of the data, the audience, and the intended goal of the communication.

Textual Data: Textual data is information represented in text form. It is used when the data is not large and can be easily comprehended by reading. It is not suitable for large data.

Tabular Data: Tabular data is organised in rows and columns, resembling a spreadsheet. Each row represent a record, and each column represents a variable or attribute.

Graphical Data: Graphical data represents information through visual elements like charts, graphs, maps and plots. It helps in understanding pattern, trends, and relationships in the data.

 

F. Competency Based Questions:

1. In Tableau, which feature allows users to filter data dynamically?

i. Parameters                                                        ii. Calculated Fields

iii. Actions                                                                         iv. Tooltips

2. Which of the following best describes “big data”?

i. Small datasets with high accuracy.                  

ii. Large volumes of data that can be processed quickly

iii. Large datasets that are difficult to process using traditional methods.

iv. Data  that is stored in a single location.

3. What is a common use of deceptive statistics in data analysis?

i. To predict future trends

ii. To summarise am describe the main features of a dataset

iii. To encrypt data

iv. To establish data integrity

 

4. When interpreting data, what does identifying trends help with?

i.        Making random decision

ii.      Understanding past behaviours and predicting future ones

iii.    Ignoring irrelevant information

iv.    Focusing only  on numerical data