🌟 SESSION 1 — BASICS OF DATA LITERACY

(Data Meaning, Importance, Types of Data, Data Points, Data Pyramid, Impact of Data Literacy)

1. What is Data?

Data refers to raw facts and figures related to an object, person, or event.
Example: 45 kg, Class 9, 78 marks, Red colour, Kolkata etc.
UNIT 2A

Though the word “data” is technically plural of “datum”, in modern usage, data is treated as singular in data science.

2. What is Data Literacy?

According to the PDF:

Data literacy is the ability to understand, interpret and communicate with data.
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Being data literate means you can:

Ask correct questions about data
Read and understand patterns
Use data to make correct decisions

★ Examples from student life:

Checking if news is fake or real
Understanding marks pattern
Making better purchases
Comparing phone prices
Tracking fitness data from smartwatch

3. Why is Data Literacy Important?

The world runs on data today — schools, supermarkets, hotels, companies, politics — all rely on data.
Your PDF says:

“The systems of the world are driven by data. So, it is imperative to be an efficient data literate.”
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Benefits of being Data Literate:

Smarter consumer – no cheating during online shopping
Informed citizen – understanding facts behind news
Career readiness – most jobs require data skills
Better personal choices – health, finance, studies

4. Understanding Information From Data

Your PDF explains:

“Logically related data becomes information.”
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Example:

Data: 45, 50, 55 → Marks
Information: Student improved over 3 tests

5. Types of Data

The PDF divides data into 2 broad types:

I. Quantitative Data

Data represented by numbers.
Examples:

Height
Weight
Age
Test scores
Temperature
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II. Qualitative Data

Descriptive, non-numeric data.
Examples:

Names
Product description
Gender
Patient history
Yes/No type answers
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6. DATA POINTS

Data is not only numbers — it includes images, audio, video, documents.
Examples:

X-ray image
CCTV video
Audio interview
Photo of a product
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These provide “additional details” beyond numbers.

7. THE DATA PYRAMID

(Explained across pages 2–3 of PDF)
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The pyramid consists of:

1️⃣ DATA

Raw facts; unprocessed values.

2️⃣ INFORMATION

When values are logically related.
Example: Correlation between attendance & marks.

3️⃣ KNOWLEDGE

Meaningful insights.
Example: Student has high marks even with low attendance → indicates special interest.

4️⃣ WISDOM

Understanding “Why”.
Example: Student loves programming → explains unusual pattern.

8. IMPACT OF DATA LITERACY

Detailed on pages 3–5.
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A. Impact on Individuals

✔ 1. Better Decision-Making

Understanding interest rates
Budgeting
Interpreting fitness app data

✔ 2. Career Growth

Many job sectors need data skills:

Marketing
Finance
Healthcare
Business

✔ 3. Critical Thinking

Detecting fake news
Understanding trends
Problem solving using patterns

✔ 4. Civic Understanding

Understanding climate data
Crime data analysis
Analysing school performance data

B. Impact on Society

✔ Economic Growth

Companies use data for innovation (example: Amazon).

✔ Better Healthcare

Personalized medicine using patient data.

✔ Better Government Policies

Using data to identify inequalities.

✔ Environmental Sustainability

Analysing water usage, pollution, energy consumption.

9. CHALLENGES OF DATA LITERACY

(From page 5)
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Accessibility (digital divide)
Privacy and ethics
Need for continuous learning
Misinformation/disinformation (as defined by UNICEF)

10. How to Become Data Literate?

(From page 6)
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To become data literate, you must learn:

Identifying correct data
Knowing data types
Cleaning data
Analysing using tools
Basics of statistics
Visualising using graphs
Understanding story behind data
Ethics & privacy
Critical thinking

11. Working Example: Healthy Habits Survey

Pages 6–8 include a complete example.
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Steps explained:

Designing survey
Collecting data
Cleaning data
Analysing averages
Creating graphs
Presenting findings

This teaches the entire data cycle in practical form.

12. DATA PRIVACY & DATA SECURITY

(Pages 8–12)
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✔ Data Privacy

Protecting personal information.

✔ Data Security

Protecting data from theft, hacking, unauthorized access.

Real-world examples from PDF:

Air India Data Breach
BigBasket Data Breach
JusPay leak
SolarWinds hack
T-Mobile data breach

(All explained on pages 12–13)

13. Threats in AI & Privacy Violations

The PDF gives three major issues:

Collecting too much personal data
Predicting sensitive information
Sharing without consent
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14. Best Practices for Cybersecurity

(Pages 10–12)
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Strong passwords
Two-factor authentication
Updating software
Avoiding phishing
Checking HTTPS
Using VPN on public Wi-Fi
Limiting app permissions
Backups
Antivirus

15. DOs & DON’Ts Table

(Pages 15–16)
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DOs:

Strong passwords
2FA
Updated software
Secure websites
Lock your screen
Connect only with trusted people

DON’Ts:

Do not share personal info
Do not download unverified apps
Do not open unknown email links
Do not copy pirated software

🌟 **UNIT 2A — SESSION 2

ACQUIRING, PROCESSING & INTERPRETING DATA (EXTREMELY DETAILED)**

📘 1. Introduction to Data in the Real World

Modern life is driven by data.
Your PDF states:

“In our daily activities we use and produce data.”
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Examples:

Clicking photographs (image data)
Fitness tracker steps (numerical data)
Online shopping history
Exam marks & attendance
Bank transactions

📘 2. Types of Data (Very Important)

Your PDF categorizes data into two major types:

A. QUALITATIVE DATA

This is descriptive (non-numeric) data.
 Examples from PDF:

Names
Gender
Address
Patient history
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✔ Explained

You cannot add, subtract, or calculate anything directly with qualitative data.
It answers “What kind?”, “Which type?”

B. QUANTITATIVE DATA

Data expressed as numbers, which can be counted or measured.
 Examples:

Body temperature
Age
House number
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✔ Explained

You can use mathematical formulas, averages, and graphs.

📘 3. Sub-Types of Quantitative Data

Your PDF goes deeper into two sub-categories:

1️⃣ Discrete Data

Countable
Whole numbers only
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✔ Examples:

Number of students in a class
Number of cars in a parking lot

✔ Explanation

You can count discrete data, but you cannot measure it on a scale.

2️⃣ Continuous Data

Measurable
Can take any value including decimals
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✔ Examples:

Height (165.5 cm)
Weight (45.3 kg)
Time

📘 4. Data in AI — How Data is Viewed by Machines

Your PDF explains that humans view data differently from AI.
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Machines require data to be:

Properly structured
Encoded
Labelled

Examples from PDF:

AI sees an image as numerical grids (pixels).
AI sees text as numerical embeddings.

📘 5. Data Acquisition — Collecting Data

One of the most important skills in AI.

Your PDF lists multiple ways of acquiring data
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A. PRIMARY DATA

You collect it directly by yourself.

✔ Methods:

Surveys
Questionnaires
Interviews
Experiments
Observations
Sensors (CCTV, fitness band, temperature sensors)
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✔ Example

Conducting a “Healthy Habits Survey” in school.

B. SECONDARY DATA

Data collected by someone else, but used by you.

✔ Sources

Websites
Databases
Journals
Apps
Social media
Government reports
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📘 6. Modern Data Collection Techniques

1️⃣ Web Scraping

Extracting data from websites using tools like:

Python BeautifulSoup
Browser extensions
API requests

Mentioned clearly in PDF.
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✔ Example:

Scraping prices of mobiles from Flipkart.

2️⃣ Data Augmentation

Used mainly in AI for improving datasets.

Your PDF says:

“Data augmentation is creating new data points from existing ones.”
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✔ Examples

For images:

Rotation
Flipping
Cropping
Color adjustment
Adding noise

For text:

Synonym replacement
Paraphrasing

✔ Why needed?

To increase training data and avoid overfitting.

3️⃣ Data Generation

Creating data using simulations.

Examples from PDF:

Weather simulation
Synthetic images
Virtual environments
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4️⃣ Sensors / IoT

Smart devices generate continuous data streams.

Examples:

Smart electricity meters
Fitness watch
CCTV
GPS
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📘 7. Understanding Quality Data (VERY IMPORTANT)

Your PDF clearly defines the qualities of “good data”:
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✔ 1. Relevance

Data must directly relate to your problem.
Example: To predict school marks, hair colour is irrelevant.

✔ 2. Accuracy

Data must be correct and error-free.

✔ 3. Completeness

No missing values.

✔ 4. Consistency

Data should not contradict itself.
Example:
Age: 15 years
Date of birth: 2023 → inconsistent.

✔ 5. Timeliness

Up-to-date data.
Old data creates wrong predictions.

✔ 6. Volume

Large amount of data helps AI learn better.

📘 8. DATA PROCESSING — CLEANING THE DATA

Your PDF gives detailed explanation of why raw data must be cleaned.
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A. Missing Values

Data may be incomplete.

✔ Example

Height column:
160, — , 158, 162

✔ Methods to fix:

Replace with average
Replace with mode
Remove row

B. Outliers (Very Important)

Your PDF gives precise definition:

“An outlier is an abnormal or unusual observation.”
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✔ Example

Marks: 85, 88, 90, 12 → 12 is an outlier.

C. Inconsistencies

Conflicting or illogical values.

Example:

Age 4 but Class 9 student
Date of birth mismatch

D. Duplicates

Same entry repeated twice.

E. Wrong Data Format

Date formats differ: 12/03/2024 vs 03-12-24.

F. Unstructured Data

Images, audio, videos → need preprocessing.

PDF explains all these clearly.
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📘 9. Tools Used for Data Processing

Your PDF lists many:
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MS Excel
Google Sheets
Python
Tableau
SQL
AI-based tools

📘 10. DATA REPRESENTATION — TABLES & CHARTS

Your PDF uses multiple graphs (bar, line, pie) to teach data visualization.
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1️⃣ Tables

Used to organize raw data.

Rows → Records
Columns → Attributes

2️⃣ Charts

✔ Bar Chart

Used for comparison.

✔ Line Chart

Used for trends over time.

✔ Pie Chart

Used for percentage distribution.

✔ Histogram

Used for frequency distribution.

✔ Scatter Plot

Used to find correlations.

📘 11. INTERPRETING DATA — MAKING SENSE OF PATTERNS

Your PDF emphasizes that simply collecting data is NOT enough.
You must interpret it.

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✔ Interpretation means:

Understanding trends
Finding reasons
Making decisions

⭐ Example from PDF — Diet & Workout Observation

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Observation:

Diet quality: High
Workout quality: Average
Steps per day: Above average

Interpretation:
Healthy habits improve well-being.

📘 12. Real-Life Case Study (PDF Example)

Pages 16–25 provide a very detailed case study.
UNIT 2A

Topics included:

Weekly health survey
Data tabular sheet
Missing values
Outlier detection
Graph plotting
Creating insights

This shows the complete data pipeline in real life.

🌟 **UNIT 2A – SESSION 3: USING DATA TO SOLVE PROBLEMS

(FULL, EXTREMELY DETAILED STUDY MATERIAL)**

This session focuses on how to interpret, analyse, and use data to make decisions in real-world situations.

⭐ 1. What Is Data Interpretation?

According to your PDF, data interpretation means:

“Relating the data logically, identifying patterns, testing ideas, and deriving insights.”
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In simple words:

Data interpretation = Understanding what the data is trying to tell us.

This helps us:

Make decisions
Solve problems
Understand trends
Predict outcomes

⭐ 2. Why Do We Need Data Interpretation?

Your PDF clearly states that interpretation helps in:

Critical thinking
Problem solving
Making informed decisions
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Examples:

✔ Teachers analyse student performance → find weak areas
✔ Doctors analyse reports → decide treatments
✔ Businesses analyse sales → choose discounts
✔ Government analyses crime data → improve safety

⭐ 3. Quantitative Data Interpretation

Your PDF explains 9 major uses of quantitative data (very important).
Pages 48–50.
UNIT 2A

Let’s explain each in simple words:

1️⃣ Comparison

Used to compare values.
Example: Comparing marks of two classes.

2️⃣ Trend Analysis

Studying how values increase or decrease over time.
Example: Temperature over a week.

3️⃣ Correlation

Finding if two values move together.
Example:
More study hours → higher marks (positive correlation)

4️⃣ Measuring Performance

Used to check improvement.
Example:
A shop checking daily sales.

5️⃣ Decision-Making

Making decisions based on numbers instead of guesswork.
Example:
Which product to restock based on highest sales.

6️⃣ Resource Allocation

Distributing resources properly.
Example:
School decides to add new teachers based on student strength.

7️⃣ Predicting Future

Using past data to predict future patterns.
Example:
Weather forecasting using temperature & humidity.

8️⃣ Understanding Distribution

Finding how values are spread.
Example:
Most students scoring 70–80 shows moderate performance.

9️⃣ Assessing Impact

Checking if something had an effect.
Example:
Did the new teaching method improve marks?

⭐ 4. Correlation vs Causation

Your PDF highlights this as an important reasoning skill.
(Implicit in analysis sections)

✔ Correlation

Two things happen together.
Example:
Ice-cream sales and temperature both increase.

✔ Causation

One thing causes the other.
Example:
More study → better marks.

Important:

Correlation does NOT always mean causation.

⭐ 5. Tools Used for Data Interpretation

PDF mentions various tools including:

Excel
Google Sheets
Tableau (detailed section p.56–59)
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Tableau is used for:

Interactive dashboards
Bar, line, scatter, pie charts
Analysing data visually

⭐ 6. Understanding Qualitative Data Interpretation

(Explained in detail on pages 42–45)
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Qualitative data means descriptive text or words (not numbers).

Steps of interpretation:

1️⃣ Data Collection

Interviews, focus groups, observations
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2️⃣ Data Organisation

Convert responses into text documents.

3️⃣ Coding & Categorising

Identify themes like:

“technical issues”
“food quality”
“service problems”

4️⃣ Analysis

Find patterns.

5️⃣ Interpretation

Understand bigger meaning.

⭐ 7. Examples from the PDF (Very Important)

Your PDF provides multiple real-world qualitative interpretation examples.

Example 1 – Student Experiences in Remote Learning

(Page 45)
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Issues found:

Technical problems
Lack of interaction
Benefit: Flexible timings
Interpretation → Improve tech support and add interactive sessions.

Example 2 – Customer Preferences for Products

(Page 45)
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Findings:

Design appealing
Price too high
Interpretation → Adjust price and simplify features.

Example 3 – Employee Satisfaction

(Page 45)
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Themes:

Work-life balance
Job satisfaction
Interpretation → Improve employee support.

⭐ 8. Combining Quantitative & Qualitative Data

Your PDF emphasises:

“Both types help understand complete picture.”
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Example:

Quantitative → 80% students dislike cafeteria food
Qualitative → Reasons include oily food, taste, hygiene

Combining both → Improves decision-making.

⭐ 9. Steps to Solve Any Problem Using Data

(Complete workflow based on your PDF)

STEP 1: Identify the Problem

Example: Students unhappy with school cafeteria.

STEP 2: Collect Data (Quantitative + Qualitative)

From:

Surveys
Interviews
Observations
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STEP 3: Clean Data

Remove:

Missing values
Duplicates
Outliers
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STEP 4: Organise Data into Tables

Attributes (columns):

Meal name
Rating
Grade
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STEP 5: Analyse Data

Use graphs:

Bar chart
Pie chart
Line graph
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STEP 6: Interpret Results

Example:
Most students prefer Rajma Chawal → Add more servings.

STEP 7: Present Findings

Using Tableau or PPT.
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⭐ 10. Common Student Errors in Data Interpretation

(Explained through exercises in PDF)
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Mistakes include:

Wrong graph selection
Ignoring outliers
Confusing correlation with causation
Misreading percentages

⭐ 11. Case Study — School Cafeteria Survey

(Pages 55–56)
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Students must:

Collect 10 survey responses
Clean the data
Find average food ratings
Identify popular/least popular items
Suggest improvements
Create graphs

⭐ 12. Why Data Interpretation Matters in AI

Data interpretation helps AI to:

Detect patterns
Make predictions
Learn from trends
Improve accuracy

Without correct interpretation → AI gives wrong results.