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🌟 SESSION 1 — BASICS OF DATA LITERACY

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

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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.

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2. What is Data Literacy?

According to the PDF:

Data literacy is the ability to understand, interpret and communicate with data.
UNIT 2A

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

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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.”
UNIT 2A

Benefits of being Data Literate:

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

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4. Understanding Information From Data

Your PDF explains:

“Logically related data becomes information.”
UNIT 2A

Example:

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

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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
  UNIT 2A

II. Qualitative Data

Descriptive, non-numeric data.
Examples:

• Names
• Product description
• Gender
• Patient history
• Yes/No type answers
  UNIT 2A

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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
  UNIT 2A

These provide “additional details” beyond numbers.

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7. THE DATA PYRAMID

(Explained across pages 2–3 of PDF)
UNIT 2A

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.

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8. IMPACT OF DATA LITERACY

Detailed on pages 3–5.
UNIT 2A

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

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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.

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9. CHALLENGES OF DATA LITERACY

(From page 5)
UNIT 2A

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

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10. How to Become Data Literate?

(From page 6)
UNIT 2A

To become data literate, you must learn:

1. Identifying correct data
2. Knowing data types
3. Cleaning data
4. Analysing using tools
5. Basics of statistics
6. Visualising using graphs
7. Understanding story behind data
8. Ethics & privacy
9. Critical thinking

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11. Working Example: Healthy Habits Survey

Pages 6–8 include a complete example.
UNIT 2A

Steps explained:

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

This teaches the entire data cycle in practical form.

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12. DATA PRIVACY & DATA SECURITY

(Pages 8–12)
UNIT 2A

✔ 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)

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13. Threats in AI & Privacy Violations

The PDF gives three major issues:

• Collecting too much personal data
• Predicting sensitive information
• Sharing without consent
  UNIT 2A

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14. Best Practices for Cybersecurity

(Pages 10–12)
UNIT 2A

• Strong passwords
• Two-factor authentication
• Updating software
• Avoiding phishing
• Checking HTTPS
• Using VPN on public Wi-Fi
• Limiting app permissions
• Backups
• Antivirus

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15. DOs & DON’Ts Table

(Pages 15–16)
UNIT 2A

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)**

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📘 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.”
UNIT 2A

Examples:

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

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📘 2. Types of Data (Very Important)

Your PDF categorizes data into two major types:

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A. QUALITATIVE DATA

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

• Names
• Gender
• Address
• Patient history
  UNIT 2A

✔ Explained

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

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B. QUANTITATIVE DATA

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

• Body temperature
• Age
• House number
  UNIT 2A

✔ Explained

You can use mathematical formulas, averages, and graphs.

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📘 3. Sub-Types of Quantitative Data

Your PDF goes deeper into two sub-categories:

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1️⃣ Discrete Data

• Countable
• Whole numbers only
  UNIT 2A

✔ 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.

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2️⃣ Continuous Data

• Measurable
• Can take any value including decimals
  UNIT 2A

✔ Examples:

• Height (165.5 cm)
• Weight (45.3 kg)
• Time

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

Your PDF explains that humans view data differently from AI.
UNIT 2A

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.

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📘 5. Data Acquisition — Collecting Data

One of the most important skills in AI.

Your PDF lists multiple ways of acquiring data
UNIT 2A

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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)
  UNIT 2A

✔ Example

Conducting a “Healthy Habits Survey” in school.

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B. SECONDARY DATA

Data collected by someone else, but used by you.

✔ Sources

• Websites
• Databases
• Journals
• Apps
• Social media
• Government reports
  UNIT 2A

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📘 6. Modern Data Collection Techniques

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1️⃣ Web Scraping

Extracting data from websites using tools like:

• Python BeautifulSoup
• Browser extensions
• API requests

Mentioned clearly in PDF.
UNIT 2A

✔ Example:

Scraping prices of mobiles from Flipkart.

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2️⃣ Data Augmentation

Used mainly in AI for improving datasets.

Your PDF says:

“Data augmentation is creating new data points from existing ones.”
UNIT 2A

✔ Examples

For images:

• Rotation
• Flipping
• Cropping
• Color adjustment
• Adding noise

For text:

• Synonym replacement
• Paraphrasing

✔ Why needed?

To increase training data and avoid overfitting.

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3️⃣ Data Generation

Creating data using simulations.

Examples from PDF:

• Weather simulation
• Synthetic images
• Virtual environments
  UNIT 2A

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4️⃣ Sensors / IoT

Smart devices generate continuous data streams.

Examples:

• Smart electricity meters
• Fitness watch
• CCTV
• GPS
  UNIT 2A

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📘 7. Understanding Quality Data (VERY IMPORTANT)

Your PDF clearly defines the qualities of “good data”:
UNIT 2A

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✔ 1. Relevance

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

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✔ 2. Accuracy

Data must be correct and error-free.

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✔ 3. Completeness

No missing values.

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✔ 4. Consistency

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

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✔ 5. Timeliness

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

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✔ 6. Volume

Large amount of data helps AI learn better.

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📘 8. DATA PROCESSING — CLEANING THE DATA

Your PDF gives detailed explanation of why raw data must be cleaned.
UNIT 2A

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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

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B. Outliers (Very Important)

Your PDF gives precise definition:

“An outlier is an abnormal or unusual observation.”
UNIT 2A

✔ Example

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

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C. Inconsistencies

Conflicting or illogical values.

Example:

• Age 4 but Class 9 student
• Date of birth mismatch

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D. Duplicates

Same entry repeated twice.

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E. Wrong Data Format

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

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F. Unstructured Data

Images, audio, videos → need preprocessing.

PDF explains all these clearly.
UNIT 2A

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📘 9. Tools Used for Data Processing

Your PDF lists many:
UNIT 2A

• MS Excel
• Google Sheets
• Python
• Tableau
• SQL
• AI-based tools

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📘 10. DATA REPRESENTATION — TABLES & CHARTS

Your PDF uses multiple graphs (bar, line, pie) to teach data visualization.
UNIT 2A

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1️⃣ Tables

Used to organize raw data.

• Rows → Records
• Columns → Attributes

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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.

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📘 11. INTERPRETING DATA — MAKING SENSE OF PATTERNS

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

UNIT 2A

✔ Interpretation means:

• Understanding trends
• Finding reasons
• Making decisions

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⭐ Example from PDF — Diet & Workout Observation

UNIT 2A

Observation:

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

Interpretation:
Healthy habits improve well-being.

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📘 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.

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⭐ 1. What Is Data Interpretation?

According to your PDF, data interpretation means:

“Relating the data logically, identifying patterns, testing ideas, and deriving insights.”
UNIT 2A

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

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⭐ 2. Why Do We Need Data Interpretation?

Your PDF clearly states that interpretation helps in:

• Critical thinking
• Problem solving
• Making informed decisions
  UNIT 2A

Examples:

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

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⭐ 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:

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1️⃣ Comparison

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

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2️⃣ Trend Analysis

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

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3️⃣ Correlation

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

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4️⃣ Measuring Performance

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

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5️⃣ Decision-Making

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

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6️⃣ Resource Allocation

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

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7️⃣ Predicting Future

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

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8️⃣ Understanding Distribution

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

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9️⃣ Assessing Impact

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

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⭐ 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.

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⭐ 5. Tools Used for Data Interpretation

PDF mentions various tools including:

• Excel
• Google Sheets
• Tableau (detailed section p.56–59)
  UNIT 2A

Tableau is used for:

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

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⭐ 6. Understanding Qualitative Data Interpretation

(Explained in detail on pages 42–45)
UNIT 2A

Qualitative data means descriptive text or words (not numbers).

Steps of interpretation:

1️⃣ Data Collection

Interviews, focus groups, observations
UNIT 2A

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.

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⭐ 7. Examples from the PDF (Very Important)

Your PDF provides multiple real-world qualitative interpretation examples.

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Example 1 – Student Experiences in Remote Learning

(Page 45)
UNIT 2A

Issues found:

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

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Example 2 – Customer Preferences for Products

(Page 45)
UNIT 2A

Findings:

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

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Example 3 – Employee Satisfaction

(Page 45)
UNIT 2A

Themes:

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

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⭐ 8. Combining Quantitative & Qualitative Data

Your PDF emphasises:

“Both types help understand complete picture.”
UNIT 2A

Example:

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

Combining both → Improves decision-making.

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⭐ 9. Steps to Solve Any Problem Using Data

(Complete workflow based on your PDF)

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STEP 1: Identify the Problem

Example: Students unhappy with school cafeteria.

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STEP 2: Collect Data (Quantitative + Qualitative)

From:

• Surveys
• Interviews
• Observations
  UNIT 2A

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STEP 3: Clean Data

Remove:

• Missing values
• Duplicates
• Outliers
  UNIT 2A

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STEP 4: Organise Data into Tables

Attributes (columns):

• Meal name
• Rating
• Grade
  UNIT 2A

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STEP 5: Analyse Data

Use graphs:

• Bar chart
• Pie chart
• Line graph
  UNIT 2A

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STEP 6: Interpret Results

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

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STEP 7: Present Findings

Using Tableau or PPT.
UNIT 2A

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⭐ 10. Common Student Errors in Data Interpretation

(Explained through exercises in PDF)
UNIT 2A

Mistakes include:

• Wrong graph selection
• Ignoring outliers
• Confusing correlation with causation
• Misreading percentages

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⭐ 11. Case Study — School Cafeteria Survey

(Pages 55–56)
UNIT 2A

Students must:

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

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⭐ 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.