CBSE Textbook: Statistical Data | Artificial Intelligence for Class 10 PDF Download

 Page 1


                  
 
Data science is a journey of exploration and discovery. Artificial Intelligence is a technology which 
completely depends on data, which is fed into the machine which makes it intelligent. And depending 
upon the type of data we have; AI can be classified into three broad domains: Data science, Computer 
Vision and Natural language processing. 
 
Data Sciences is a concept to unify statistics, data analysis, machine learning and their related 
methods in order to understand and analyze actual phenomena with data. It employs techniques 
andtheories drawn from many fields within the context of Mathematics, Statistics, Computer Science, 
and Information Science. 
 
 
Applications of Data Science: 
 
 
Internet Search: All the search engines (including Google) make use of data science algorithms to 
deliver the best result for our searched query in the fraction of a second. Considering the fact that 
Google processes more than 20 petabytes of data every day, had there been no data science, Google 
wouldn’t have been the ‘Google’ we know today. 
 
 
 
Targeted Advertising: If you thought Search would have been 
the biggest of all data science applications, here is a 
challenger – the entire digital marketing spectrum. Starting 
from the display banners on various websites to the digital 
billboards at the airports – almost all of them are decided by 
using data science algorithms. This is the reason why digital 
ads have been able to get a much higher CTR (Call-Through 
Rate) than traditional advertisements. They can be targeted 
based on a user’s past behaviour. 
 
 
 
Website Recommendations: Aren’t we all used to the suggestions about similar products on Amazon? 
They not only help us find relevant products from billions of products available with them but also add 
a lot to the user experience. A lot of companies have fervidly used this engine to 
promote their products in accordance with the user’s interest and relevance of information. Internet 
giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB and many more use this system to 
improve the user experience. The recommendations are made based on previous search results for a user. 
 
 
 
 
 
Page 2


                  
 
Data science is a journey of exploration and discovery. Artificial Intelligence is a technology which 
completely depends on data, which is fed into the machine which makes it intelligent. And depending 
upon the type of data we have; AI can be classified into three broad domains: Data science, Computer 
Vision and Natural language processing. 
 
Data Sciences is a concept to unify statistics, data analysis, machine learning and their related 
methods in order to understand and analyze actual phenomena with data. It employs techniques 
andtheories drawn from many fields within the context of Mathematics, Statistics, Computer Science, 
and Information Science. 
 
 
Applications of Data Science: 
 
 
Internet Search: All the search engines (including Google) make use of data science algorithms to 
deliver the best result for our searched query in the fraction of a second. Considering the fact that 
Google processes more than 20 petabytes of data every day, had there been no data science, Google 
wouldn’t have been the ‘Google’ we know today. 
 
 
 
Targeted Advertising: If you thought Search would have been 
the biggest of all data science applications, here is a 
challenger – the entire digital marketing spectrum. Starting 
from the display banners on various websites to the digital 
billboards at the airports – almost all of them are decided by 
using data science algorithms. This is the reason why digital 
ads have been able to get a much higher CTR (Call-Through 
Rate) than traditional advertisements. They can be targeted 
based on a user’s past behaviour. 
 
 
 
Website Recommendations: Aren’t we all used to the suggestions about similar products on Amazon? 
They not only help us find relevant products from billions of products available with them but also add 
a lot to the user experience. A lot of companies have fervidly used this engine to 
promote their products in accordance with the user’s interest and relevance of information. Internet 
giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB and many more use this system to 
improve the user experience. The recommendations are made based on previous search results for a user. 
 
 
 
 
 
                  
 
Genetics & Genomics: 
 
Data Science applications also enable an advanced level 
of treatment personalization through research in 
genetics and genomics. Data science techniques allow 
integration of different kinds of data with genomic data 
in disease research, which provides a deeper 
understanding of genetic issues in reactions to particular 
drugs and diseases. As soon as we acquire reliable 
personal genome data, we will achieve a deeper 
understanding of human DNA. The advanced genetic risk 
prediction will be a major step towards more individual 
care. 
 
 
 
 
 
 
 
Introduction to Low/No-Code AI approach for Statistical Data 
Let’s say you want to build a product, food delivery application. How do you go about starting it? 
 
 
Building a food delivery application involves several steps, from conceptualization to development, 
testing, and deployment. The 3 most popular approaches to code are given below. 
Activity: Word Scramble the terms related to AI applications. 
 
Purpose: Recall of AI terms 
 
VANAGTOINI APP      
UALTIRV SSISATANT     
AGEGUALAN TIONSLATRAN     
Page 3


                  
 
Data science is a journey of exploration and discovery. Artificial Intelligence is a technology which 
completely depends on data, which is fed into the machine which makes it intelligent. And depending 
upon the type of data we have; AI can be classified into three broad domains: Data science, Computer 
Vision and Natural language processing. 
 
Data Sciences is a concept to unify statistics, data analysis, machine learning and their related 
methods in order to understand and analyze actual phenomena with data. It employs techniques 
andtheories drawn from many fields within the context of Mathematics, Statistics, Computer Science, 
and Information Science. 
 
 
Applications of Data Science: 
 
 
Internet Search: All the search engines (including Google) make use of data science algorithms to 
deliver the best result for our searched query in the fraction of a second. Considering the fact that 
Google processes more than 20 petabytes of data every day, had there been no data science, Google 
wouldn’t have been the ‘Google’ we know today. 
 
 
 
Targeted Advertising: If you thought Search would have been 
the biggest of all data science applications, here is a 
challenger – the entire digital marketing spectrum. Starting 
from the display banners on various websites to the digital 
billboards at the airports – almost all of them are decided by 
using data science algorithms. This is the reason why digital 
ads have been able to get a much higher CTR (Call-Through 
Rate) than traditional advertisements. They can be targeted 
based on a user’s past behaviour. 
 
 
 
Website Recommendations: Aren’t we all used to the suggestions about similar products on Amazon? 
They not only help us find relevant products from billions of products available with them but also add 
a lot to the user experience. A lot of companies have fervidly used this engine to 
promote their products in accordance with the user’s interest and relevance of information. Internet 
giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB and many more use this system to 
improve the user experience. The recommendations are made based on previous search results for a user. 
 
 
 
 
 
                  
 
Genetics & Genomics: 
 
Data Science applications also enable an advanced level 
of treatment personalization through research in 
genetics and genomics. Data science techniques allow 
integration of different kinds of data with genomic data 
in disease research, which provides a deeper 
understanding of genetic issues in reactions to particular 
drugs and diseases. As soon as we acquire reliable 
personal genome data, we will achieve a deeper 
understanding of human DNA. The advanced genetic risk 
prediction will be a major step towards more individual 
care. 
 
 
 
 
 
 
 
Introduction to Low/No-Code AI approach for Statistical Data 
Let’s say you want to build a product, food delivery application. How do you go about starting it? 
 
 
Building a food delivery application involves several steps, from conceptualization to development, 
testing, and deployment. The 3 most popular approaches to code are given below. 
Activity: Word Scramble the terms related to AI applications. 
 
Purpose: Recall of AI terms 
 
VANAGTOINI APP      
UALTIRV SSISATANT     
AGEGUALAN TIONSLATRAN     
                  
 
 
 
Custom code is also known as high code. 
How do we choose? Which of these 3 is the most suitable for our app? 
 
High code Low code No code 
High code development refers 
to traditional software 
development where 
programmers write code 
manually using programming 
languages like Java, Python, C#, 
etc. 
Low code development 
involves using platforms or 
tools that provide visual 
interfaces and pre-built 
components to streamline the 
application development 
process. 
No code development takes 
low code principles further by 
allowing users to create 
applications without any 
coding or scripting knowledge. 
A team of software coders 
need to write all the code 
manually. 
Programmers need to write 
some code manually. 
Coding knowledge is not 
required; hence anyone can 
make the product. 
It is expensive. It is less expensive compared 
to high code. 
It is less expensive compared 
low code. 
The company can own the 
product they create. You can 
create anything and customise 
your product in any way. 
You can customise your 
product to an extent only using 
code. 
For example, custom chatbot. 
Lack of customisable options as 
No-Code AI tools are limited to 
functions in the tool. 
Simple to use as it uses drag- 
and-drop features instead of 
coding. 
Now that we have seen the differences, 
which approach do you think is the most suitable one for our Food Delivery app? Discuss! 
 
 
Page 4


                  
 
Data science is a journey of exploration and discovery. Artificial Intelligence is a technology which 
completely depends on data, which is fed into the machine which makes it intelligent. And depending 
upon the type of data we have; AI can be classified into three broad domains: Data science, Computer 
Vision and Natural language processing. 
 
Data Sciences is a concept to unify statistics, data analysis, machine learning and their related 
methods in order to understand and analyze actual phenomena with data. It employs techniques 
andtheories drawn from many fields within the context of Mathematics, Statistics, Computer Science, 
and Information Science. 
 
 
Applications of Data Science: 
 
 
Internet Search: All the search engines (including Google) make use of data science algorithms to 
deliver the best result for our searched query in the fraction of a second. Considering the fact that 
Google processes more than 20 petabytes of data every day, had there been no data science, Google 
wouldn’t have been the ‘Google’ we know today. 
 
 
 
Targeted Advertising: If you thought Search would have been 
the biggest of all data science applications, here is a 
challenger – the entire digital marketing spectrum. Starting 
from the display banners on various websites to the digital 
billboards at the airports – almost all of them are decided by 
using data science algorithms. This is the reason why digital 
ads have been able to get a much higher CTR (Call-Through 
Rate) than traditional advertisements. They can be targeted 
based on a user’s past behaviour. 
 
 
 
Website Recommendations: Aren’t we all used to the suggestions about similar products on Amazon? 
They not only help us find relevant products from billions of products available with them but also add 
a lot to the user experience. A lot of companies have fervidly used this engine to 
promote their products in accordance with the user’s interest and relevance of information. Internet 
giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB and many more use this system to 
improve the user experience. The recommendations are made based on previous search results for a user. 
 
 
 
 
 
                  
 
Genetics & Genomics: 
 
Data Science applications also enable an advanced level 
of treatment personalization through research in 
genetics and genomics. Data science techniques allow 
integration of different kinds of data with genomic data 
in disease research, which provides a deeper 
understanding of genetic issues in reactions to particular 
drugs and diseases. As soon as we acquire reliable 
personal genome data, we will achieve a deeper 
understanding of human DNA. The advanced genetic risk 
prediction will be a major step towards more individual 
care. 
 
 
 
 
 
 
 
Introduction to Low/No-Code AI approach for Statistical Data 
Let’s say you want to build a product, food delivery application. How do you go about starting it? 
 
 
Building a food delivery application involves several steps, from conceptualization to development, 
testing, and deployment. The 3 most popular approaches to code are given below. 
Activity: Word Scramble the terms related to AI applications. 
 
Purpose: Recall of AI terms 
 
VANAGTOINI APP      
UALTIRV SSISATANT     
AGEGUALAN TIONSLATRAN     
                  
 
 
 
Custom code is also known as high code. 
How do we choose? Which of these 3 is the most suitable for our app? 
 
High code Low code No code 
High code development refers 
to traditional software 
development where 
programmers write code 
manually using programming 
languages like Java, Python, C#, 
etc. 
Low code development 
involves using platforms or 
tools that provide visual 
interfaces and pre-built 
components to streamline the 
application development 
process. 
No code development takes 
low code principles further by 
allowing users to create 
applications without any 
coding or scripting knowledge. 
A team of software coders 
need to write all the code 
manually. 
Programmers need to write 
some code manually. 
Coding knowledge is not 
required; hence anyone can 
make the product. 
It is expensive. It is less expensive compared 
to high code. 
It is less expensive compared 
low code. 
The company can own the 
product they create. You can 
create anything and customise 
your product in any way. 
You can customise your 
product to an extent only using 
code. 
For example, custom chatbot. 
Lack of customisable options as 
No-Code AI tools are limited to 
functions in the tool. 
Simple to use as it uses drag- 
and-drop features instead of 
coding. 
Now that we have seen the differences, 
which approach do you think is the most suitable one for our Food Delivery app? Discuss! 
 
 
                  
 
 
 
 
Can you think of an invention that has made life easier in terms of saving time/cost for you? 
Some inventions that have made life today easier are smartphones, credit cards, internet, online streaming 
services, Refrigeration technology, GPS navigation, medical innovations etc. 
Similar to those inventions, let’s look at how No-Code AI makes our lives easier! 
 
 
 
 
 
More code to test out different algorithms… And 
more code to pick the best algorithm… 
 
 
 
 
 
 
 
 
No-Code 
? In No-Code AI, we can drag and drop, these 
models in few seconds. 
? No coding knowledge is required to implement 
complex ML algorithms 
? Drag and drop feature of a No-Code tool makes 
it easier. 
 
 
 
 
 
 
 
 
 
That’s a lot of code, right? 
And that’s why we have No- 
Code AI. 
Page 5


                  
 
Data science is a journey of exploration and discovery. Artificial Intelligence is a technology which 
completely depends on data, which is fed into the machine which makes it intelligent. And depending 
upon the type of data we have; AI can be classified into three broad domains: Data science, Computer 
Vision and Natural language processing. 
 
Data Sciences is a concept to unify statistics, data analysis, machine learning and their related 
methods in order to understand and analyze actual phenomena with data. It employs techniques 
andtheories drawn from many fields within the context of Mathematics, Statistics, Computer Science, 
and Information Science. 
 
 
Applications of Data Science: 
 
 
Internet Search: All the search engines (including Google) make use of data science algorithms to 
deliver the best result for our searched query in the fraction of a second. Considering the fact that 
Google processes more than 20 petabytes of data every day, had there been no data science, Google 
wouldn’t have been the ‘Google’ we know today. 
 
 
 
Targeted Advertising: If you thought Search would have been 
the biggest of all data science applications, here is a 
challenger – the entire digital marketing spectrum. Starting 
from the display banners on various websites to the digital 
billboards at the airports – almost all of them are decided by 
using data science algorithms. This is the reason why digital 
ads have been able to get a much higher CTR (Call-Through 
Rate) than traditional advertisements. They can be targeted 
based on a user’s past behaviour. 
 
 
 
Website Recommendations: Aren’t we all used to the suggestions about similar products on Amazon? 
They not only help us find relevant products from billions of products available with them but also add 
a lot to the user experience. A lot of companies have fervidly used this engine to 
promote their products in accordance with the user’s interest and relevance of information. Internet 
giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB and many more use this system to 
improve the user experience. The recommendations are made based on previous search results for a user. 
 
 
 
 
 
                  
 
Genetics & Genomics: 
 
Data Science applications also enable an advanced level 
of treatment personalization through research in 
genetics and genomics. Data science techniques allow 
integration of different kinds of data with genomic data 
in disease research, which provides a deeper 
understanding of genetic issues in reactions to particular 
drugs and diseases. As soon as we acquire reliable 
personal genome data, we will achieve a deeper 
understanding of human DNA. The advanced genetic risk 
prediction will be a major step towards more individual 
care. 
 
 
 
 
 
 
 
Introduction to Low/No-Code AI approach for Statistical Data 
Let’s say you want to build a product, food delivery application. How do you go about starting it? 
 
 
Building a food delivery application involves several steps, from conceptualization to development, 
testing, and deployment. The 3 most popular approaches to code are given below. 
Activity: Word Scramble the terms related to AI applications. 
 
Purpose: Recall of AI terms 
 
VANAGTOINI APP      
UALTIRV SSISATANT     
AGEGUALAN TIONSLATRAN     
                  
 
 
 
Custom code is also known as high code. 
How do we choose? Which of these 3 is the most suitable for our app? 
 
High code Low code No code 
High code development refers 
to traditional software 
development where 
programmers write code 
manually using programming 
languages like Java, Python, C#, 
etc. 
Low code development 
involves using platforms or 
tools that provide visual 
interfaces and pre-built 
components to streamline the 
application development 
process. 
No code development takes 
low code principles further by 
allowing users to create 
applications without any 
coding or scripting knowledge. 
A team of software coders 
need to write all the code 
manually. 
Programmers need to write 
some code manually. 
Coding knowledge is not 
required; hence anyone can 
make the product. 
It is expensive. It is less expensive compared 
to high code. 
It is less expensive compared 
low code. 
The company can own the 
product they create. You can 
create anything and customise 
your product in any way. 
You can customise your 
product to an extent only using 
code. 
For example, custom chatbot. 
Lack of customisable options as 
No-Code AI tools are limited to 
functions in the tool. 
Simple to use as it uses drag- 
and-drop features instead of 
coding. 
Now that we have seen the differences, 
which approach do you think is the most suitable one for our Food Delivery app? Discuss! 
 
 
                  
 
 
 
 
Can you think of an invention that has made life easier in terms of saving time/cost for you? 
Some inventions that have made life today easier are smartphones, credit cards, internet, online streaming 
services, Refrigeration technology, GPS navigation, medical innovations etc. 
Similar to those inventions, let’s look at how No-Code AI makes our lives easier! 
 
 
 
 
 
More code to test out different algorithms… And 
more code to pick the best algorithm… 
 
 
 
 
 
 
 
 
No-Code 
? In No-Code AI, we can drag and drop, these 
models in few seconds. 
? No coding knowledge is required to implement 
complex ML algorithms 
? Drag and drop feature of a No-Code tool makes 
it easier. 
 
 
 
 
 
 
 
 
 
That’s a lot of code, right? 
And that’s why we have No- 
Code AI. 
                  
 
Why do we need No-Code AI? 
 
? We tend to run into many types of errors when we are coding, and it can be very 
troublesome at times. 
? In No-Code AI since we do not need to code, we won’t have any code errors! 
? No-Code AI helps to save cost for businesses as it is costly to implement completely 
coded AI systems. 
? Companies can implement AI with less stress and without the need to hire an AI staff 
with No-Code AI. 
? No-Code AI is easy to use – even middle school students can create AI using No-Code 
tools 
? Since it has visual & drag-and-drop features, anyone can see what they are building in 
real-time 
 
Who can use No-Code AI? 
? No-Code AI makes AI more accessible to the general public. 
? Non-technical people such as doctors, architects, musicians 
may quickly construct accurate AI models with no coding involved. 
 
 
Let’s look at a scenario to understand who can use No-Code AI 
? No-Code AI makes AI more accessible to the general public. 
? Non-technical people such as doctors, architects, musicians 
may quickly construct accurate AI models with no coding involved. 
Thus No-Code AI can empower individuals and organizations across various industries and skill 
levels to harness the potential of artificial intelligence for their specific needs. 
Let’s look at a scenario to understand who can use No-Code AI. 
 
 
 
 
 
 
 
 
 
Problem: Kayla is a wildlife animal’s dietitian 
manager at the zoo. She takes care of the cost of 
buying meat and vegetables for animals. With the 
prices of food increasing rapidly, it will become 
more expensive for the zoo to buy healthy and 
nutritious foods for its animals. Therefore, the 
zoo’s accounts team wants to know the increase in 
the price of food so that they can ask the 
government or sponsors to fund for the food. Thus, 
Kayla requires the help of AI to predict the price.