(Data Meaning, Importance, Types of Data, Data Points, Data Pyramid, Impact of Data Literacy)
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.
According to the PDF:
Data literacy is the ability to understand, interpret and communicate with data.
UNIT 2A
Being data literate means you can:
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
Your PDF explains:
“Logically related data becomes information.”
UNIT 2A
Example:
The PDF divides data into 2 broad types:
Data represented by numbers.
Examples:
Descriptive, non-numeric data.
Examples:
Data is not only numbers — it includes images, audio, video, documents.
Examples:
These provide “additional details” beyond numbers.
(Explained across pages 2–3 of PDF)
UNIT 2A
The pyramid consists of:
Raw facts; unprocessed values.
When values are logically related.
Example: Correlation between attendance & marks.
Meaningful insights.
Example: Student has high marks even with low attendance → indicates special interest.
Understanding “Why”.
Example: Student loves programming → explains unusual pattern.
Detailed on pages 3–5.
UNIT 2A
Many job sectors need data skills:
Companies use data for innovation (example: Amazon).
Personalized medicine using patient data.
Using data to identify inequalities.
Analysing water usage, pollution, energy consumption.
(From page 5)
UNIT 2A
(From page 6)
UNIT 2A
To become data literate, you must learn:
Pages 6–8 include a complete example.
UNIT 2A
Steps explained:
This teaches the entire data cycle in practical form.
(Pages 8–12)
UNIT 2A
Protecting personal information.
Protecting data from theft, hacking, unauthorized access.
(All explained on pages 12–13)
The PDF gives three major issues:
(Pages 10–12)
UNIT 2A
(Pages 15–16)
UNIT 2A
DOs:
DON’Ts:
ACQUIRING, PROCESSING & INTERPRETING DATA (EXTREMELY DETAILED)**
Modern life is driven by data.
Your PDF states:
“In our daily activities we use and produce data.”
UNIT 2A
Examples:
Your PDF categorizes data into two major types:
This is descriptive (non-numeric) data.
Examples from PDF:
You cannot add, subtract, or calculate anything directly with qualitative data.
It answers “What kind?”, “Which type?”
Data expressed as numbers, which can be counted or measured.
Examples:
You can use mathematical formulas, averages, and graphs.
Your PDF goes deeper into two sub-categories:
You can count discrete data, but you cannot measure it on a scale.
Your PDF explains that humans view data differently from AI.
UNIT 2A
Machines require data to be:
Examples from PDF:
One of the most important skills in AI.
Your PDF lists multiple ways of acquiring data
UNIT 2A
You collect it directly by yourself.
Conducting a “Healthy Habits Survey” in school.
Data collected by someone else, but used by you.
Extracting data from websites using tools like:
Mentioned clearly in PDF.
UNIT 2A
Scraping prices of mobiles from Flipkart.
Used mainly in AI for improving datasets.
Your PDF says:
“Data augmentation is creating new data points from existing ones.”
UNIT 2A
For images:
For text:
To increase training data and avoid overfitting.
Creating data using simulations.
Examples from PDF:
Smart devices generate continuous data streams.
Examples:
Your PDF clearly defines the qualities of “good data”:
UNIT 2A
Data must directly relate to your problem.
Example: To predict school marks, hair colour is irrelevant.
Data must be correct and error-free.
No missing values.
Data should not contradict itself.
Example:
Age: 15 years
Date of birth: 2023 → inconsistent.
Up-to-date data.
Old data creates wrong predictions.
Large amount of data helps AI learn better.
Your PDF gives detailed explanation of why raw data must be cleaned.
UNIT 2A
Data may be incomplete.
Height column:
160, — , 158, 162
Your PDF gives precise definition:
“An outlier is an abnormal or unusual observation.”
UNIT 2A
Marks: 85, 88, 90, 12 → 12 is an outlier.
Conflicting or illogical values.
Example:
Same entry repeated twice.
Date formats differ: 12/03/2024 vs 03-12-24.
Images, audio, videos → need preprocessing.
PDF explains all these clearly.
UNIT 2A
Your PDF lists many:
UNIT 2A
Your PDF uses multiple graphs (bar, line, pie) to teach data visualization.
UNIT 2A
Used to organize raw data.
Used for comparison.
Used for trends over time.
Used for percentage distribution.
Used for frequency distribution.
Used to find correlations.
Your PDF emphasizes that simply collecting data is NOT enough.
You must interpret it.
UNIT 2A
UNIT 2A
Observation:
Interpretation:
Healthy habits improve well-being.
Pages 16–25 provide a very detailed case study.
UNIT 2A
Topics included:
This shows the complete data pipeline in real life.
(FULL, EXTREMELY DETAILED STUDY MATERIAL)**
This session focuses on how to interpret, analyse, and use data to make decisions in real-world situations.
According to your PDF, data interpretation means:
“Relating the data logically, identifying patterns, testing ideas, and deriving insights.”
UNIT 2A
Data interpretation = Understanding what the data is trying to tell us.
This helps us:
Your PDF clearly states that interpretation helps in:
✔ Teachers analyse student performance → find weak areas
✔ Doctors analyse reports → decide treatments
✔ Businesses analyse sales → choose discounts
✔ Government analyses crime data → improve safety
Your PDF explains 9 major uses of quantitative data (very important).
Pages 48–50.
UNIT 2A
Let’s explain each in simple words:
Used to compare values.
Example: Comparing marks of two classes.
Studying how values increase or decrease over time.
Example: Temperature over a week.
Finding if two values move together.
Example:
More study hours → higher marks (positive correlation)
Used to check improvement.
Example:
A shop checking daily sales.
Making decisions based on numbers instead of guesswork.
Example:
Which product to restock based on highest sales.
Distributing resources properly.
Example:
School decides to add new teachers based on student strength.
Using past data to predict future patterns.
Example:
Weather forecasting using temperature & humidity.
Finding how values are spread.
Example:
Most students scoring 70–80 shows moderate performance.
Checking if something had an effect.
Example:
Did the new teaching method improve marks?
Your PDF highlights this as an important reasoning skill.
(Implicit in analysis sections)
Two things happen together.
Example:
Ice-cream sales and temperature both increase.
One thing causes the other.
Example:
More study → better marks.
Correlation does NOT always mean causation.
PDF mentions various tools including:
(Explained in detail on pages 42–45)
UNIT 2A
Qualitative data means descriptive text or words (not numbers).
Interviews, focus groups, observations
UNIT 2A
Convert responses into text documents.
Identify themes like:
Find patterns.
Understand bigger meaning.
Your PDF provides multiple real-world qualitative interpretation examples.
(Page 45)
UNIT 2A
Issues found:
(Page 45)
UNIT 2A
Findings:
(Page 45)
UNIT 2A
Themes:
Your PDF emphasises:
“Both types help understand complete picture.”
UNIT 2A
Combining both → Improves decision-making.
(Complete workflow based on your PDF)
Example: Students unhappy with school cafeteria.
From:
Remove:
Attributes (columns):
Use graphs:
Example:
Most students prefer Rajma Chawal → Add more servings.
Using Tableau or PPT.
UNIT 2A
(Explained through exercises in PDF)
UNIT 2A
Mistakes include:
(Pages 55–56)
UNIT 2A
Students must:
Data interpretation helps AI to:
Without correct interpretation → AI gives wrong results.
The CBSE Class 9 Syllabus 2026–27 has been updated according to the latest NCERT curriculum…
The ISRO YUVIKA 2026 (Young Scientist Program) offers Class 9 students a unique opportunity to…
Looking for effective exam preparation? This CBSE Class 8 Science Practice Paper 2026 with Answer…
Looking for effective exam preparation? This CBSE Class 8 Science Practice Paper 2026 with Answer…
Looking for effective exam preparation? This CBSE Class 8 Science Practice Paper 2026 with Answer…
Access the Chapter-wise NCERT Solutions for Class 1 Maths to enhance your learning experience and…