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Artificial Intelligence / Data Literacy

Acquiring Data, Processing and Interpreting Data

1. Acquiring Data

Data acquisition means collecting data from different sources for a specific purpose.

For example, if we want to know which subject students like most, we can collect information by asking students to fill out a survey.

Steps involved in acquiring data

1. Identify the purpose – Decide why the data is required.
2. Identify the required data – Decide what information needs to be collected.
3. Select the source – Decide where the data will come from.
4. Collect the data – Use surveys, interviews, observations, etc.
5. Store the data – Save the collected data in a proper format.

Example

Purpose: To find the favorites sport of students.

Student	Favorites Sport
Rahul	Cricket
Anjali	Badminton
Aman	Football
Priya	Cricket

This table contains the collected data.

2. Best Practices for Acquiring Data

While collecting data, we should follow some good practices.

1. Define the purpose clearly

Know exactly why the data is required.

2. Collect relevant data

Collect only the information needed for the task.

3. Use reliable sources

Data should come from trustworthy sources.

4. Avoid bias

Questions and methods should not unfairly influence the results.

5. Respect privacy

Personal information should be collected and used responsibly.

6. Take consent when required

People should know why their information is being collected.

7. Ensure accuracy

Check the data for mistakes while collecting it.

8. Keep data secure

Protect data from unauthorized access or misuse.

9. Record data systematically

Use tables, spreadsheets, databases, or other organized formats.

3. Features / Characteristics of Good Data

Good data should have certain characteristics.

1. Accuracy

Data should be correct and free from errors.

2. Relevance

Data should be related to the purpose.

3. Completeness

Important required information should not be missing.

4. Consistency

Data should follow the same format and rules.

5. Timeliness

Data should be up-to-date.

6. Reliability

Data should come from a trustworthy source.

7. Accessibility

Authorized users should be able to access the data when needed.

Example

If a school wants to analyses current student attendance, attendance data from five years ago would not be very useful.

4. Data Preprocessing

Data preprocessing is the process of cleaning and preparing raw data before it is analysed or used by a computer system.

Raw data may contain:

• Missing values
• Duplicate records
• Spelling mistakes
• Incorrect values
• Different formats
• Unnecessary information

Main steps of data preprocessing

Raw Data → Cleaning → Organizing → Transforming → Ready for Analysis

1. Data Cleaning

Removing or correcting errors from data.

Example:

Before:

14, 15, 16, “fifteen”, 17

After:

14, 15, 16, 15, 17

1. Removing Duplicates

Repeated records are identified and removed.

1. Handling Missing Values

Missing information can be:

• Filled using appropriate values
• Obtained again
• Left blank when appropriate
• Removed if necessary

1. Standardizing Data

Putting data into a common format.

Example:

Male, M, male

can be standardized as:

Male

5. Importance of Data

Data plays an important role in our daily lives and in Artificial Intelligence.

Importance of data:

1. Helps in decision-making
2. Helps identify patterns and trends
3. Supports scientific research
4. Helps businesses understand customers
5. Improves educational planning
6. Helps AI systems learn
7. Helps predict future outcomes
8. Helps solve problems

Example

A school can analyses students’ examination data to identify subjects where students need additional support.

6. Processing Data

Data processing means converting raw data into a meaningful and useful form.

Basic process:

Data Collection → Data Cleaning → Data Processing → Analysis → Interpretation → Decision

Example

Suppose the marks of five students are:

70, 80, 90, 60, 75

After processing, we can calculate:

• Total marks
• Average marks
• Highest marks
• Lowest marks

This makes the raw data more useful.

7. Data Interpretation

Data interpretation means understanding and explaining what the processed data tells us.

It helps us answer questions such as:

• What does the data show?
• What pattern can we identify?
• What conclusion can we draw?
• What decision can be made?

Example

Suppose the marks are:

Student	Marks
A	60
B	75
C	90
D	80
E	95

From this data, we can interpret:

Student E obtained the highest marks, while Student A obtained the lowest marks.

8. Ways to Interpret Data

Data can be interpreted using:

1. Tables

Tables arrange data in rows and columns.

2. Bar Graph

Used to compare different categories.

Example:

• Number of students in different classes
• Number of students choosing different subjects

3. Pie Chart

Used to show parts of a whole, usually in percentages.

4. Line Graph

Used to show changes or trends over time.

Example:

• Temperature over seven days
• Monthly school attendance

5. Histogram

Used to represent the distribution of numerical data.

 

9. Tools Used for Data Interpretation

Several digital tools can be used to process and interpret data.

1. Microsoft Excel

Used for:

• Entering data
• Calculations
• Sorting and filtering
• Creating charts and graphs
• Analyzing data

2. Google Sheets

An online spreadsheet tool used to:

• Store data
• Perform calculations
• Create charts
• Collaborate with others

3. Microsoft Power BI

Used for:

• Data analysis
• Interactive dashboards
• Charts and visualizations
• Reporting

4. Tableau

A data visualization tool used to create:

• Graphs
• Charts
• Dashboards
• Reports

5. Python

Python can be used for:

• Data processing
• Data analysis
• Visualisation
• AI and Machine Learning

Some commonly used Python libraries are:

• Pandas – data handling
• NumPy – numerical operations
• Matplotlib – graphs and charts
  

10. Data Visualisation

Data visualization means representing data using graphs, charts, diagrams, or other visual forms.

It makes complex information easier to understand.

Example

Instead of writing:

Class 6 = 40 students
Class 7 = 45 students
Class 8 = 38 students
Class 9 = 42 students

we can represent the information using a bar graph.

Benefits of data visualization

• Easy to understand
• Helps identify patterns
• Helps compare information
• Makes trends visible
• Supports better decision-making

11. Data in Artificial Intelligence

Data is extremely important in Artificial Intelligence (AI).

AI systems use data to learn patterns and make predictions or decisions.

Example: Face Recognition

A face-recognition system may be trained using many images of faces.

Images → Data Processing → AI Training → Pattern Recognition → Prediction

Example: Recommendation Systems

Online platforms can use information about users’ previous activities to recommend:

• Videos
• Music
• Products
• Articles

Therefore:

Data is one of the most important resources for Artificial Intelligence.

 

12. Data Processing Cycle

You can remember the complete process as:

COLLECT → CLEAN → PROCESS → ANALYSE → INTERPRET → DECIDE

Stage	Meaning
Collect	Gather data
Clean	Remove errors
Process	Organize and transform
Analyze	Find patterns and relationships
Interpret	Understand the results
Decide	Use results for decision-making

 

13. Important Terms for Examination

Data: Raw facts and information.

Data Acquisition: Process of collecting data.

Primary Data: Data collected directly for a specific purpose.

Secondary Data: Data collected previously by another person or organization.

Data Processing: Converting raw data into a useful form.

Data Preprocessing: Cleaning and preparing data before analysis.

Data Interpretation: Understanding and explaining the meaning of data.

Data Visualisation: Representing data using graphs, charts, and diagrams.

Qualitative Data: Non-numerical data describing qualities or characteristics.

Quantitative Data: Numerical data that can be counted or measured.