Data Sciences | Artificial Intelligence for Class 10 PDF Download

 Page 1


 
 
Data Sciences 
Introduction 
As we have discussed earlier in class 9, Artificial Intelligence is a technology which completely depends 
on data. It is the 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: 
 
Each domain has its own type of data which gets fed into the machine and hence has its own way of 
working around it. Talking about Data Sciences, it is a concept to unify statistics, data analysis, machine 
learning and their related methods in order to understand and analyse actual phenomena with data. 
It employs techniques and theories drawn from many fields within the context of Mathematics, 
Statistics, Computer Science, and Information Science. 
Now before we get into the concepts of Data Sciences, let us experience this domain with the help of 
the following game: 
 
* Rock, Paper & Scissors: https://www.afiniti.com/corporate/rock-paper-
scissors 
 
Go to this link and try to play the game of Rock, Paper Scissors against an AI model. The challenge here 
is to win 20 games against AI before AI wins them against you. 
Did you manage to win? 
__________________________________________________________________________________
__________________________________________________________________________________ 
  
Data
• Data Sciences
• Working around numeric and alpha-numeric data.
CV
• Computer Vision
• Working around image and visual data.
NLP
• Natural Language Processing
• Working around textual and speech-based data.
Page 2


 
 
Data Sciences 
Introduction 
As we have discussed earlier in class 9, Artificial Intelligence is a technology which completely depends 
on data. It is the 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: 
 
Each domain has its own type of data which gets fed into the machine and hence has its own way of 
working around it. Talking about Data Sciences, it is a concept to unify statistics, data analysis, machine 
learning and their related methods in order to understand and analyse actual phenomena with data. 
It employs techniques and theories drawn from many fields within the context of Mathematics, 
Statistics, Computer Science, and Information Science. 
Now before we get into the concepts of Data Sciences, let us experience this domain with the help of 
the following game: 
 
* Rock, Paper & Scissors: https://www.afiniti.com/corporate/rock-paper-
scissors 
 
Go to this link and try to play the game of Rock, Paper Scissors against an AI model. The challenge here 
is to win 20 games against AI before AI wins them against you. 
Did you manage to win? 
__________________________________________________________________________________
__________________________________________________________________________________ 
  
Data
• Data Sciences
• Working around numeric and alpha-numeric data.
CV
• Computer Vision
• Working around image and visual data.
NLP
• Natural Language Processing
• Working around textual and speech-based data.
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
What was the strategy that you applied to win this game against the AI machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Was it different playing Rock, Paper & Scissors with an AI machine as compared to a human? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach was the machine following while playing against you? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Data Sciences 
Data Science is not a new field. Data Sciences majorly work around analysing the data and when it 
comes to AI, the analysis helps in making the machine intelligent enough to perform tasks by itself. 
There exist various applications of Data Science in today’s world. Some of them are: 
Fraud and Risk Detection*: The earliest applications of data 
science were in Finance. Companies were fed up of bad debts and 
losses every year. However, they had a lot of data which use to get 
collected during the initial paperwork while sanctioning loans. They 
decided to bring in data scientists in order to rescue them from 
losses. 
Over the years, banking companies learned to divide and conquer 
data via customer profiling, past expenditures, and other essential 
variables to analyse the probabilities of risk and default. Moreover, 
it also helped them to push their banking products based on 
customer’s purchasing power. 
 
Genetics & Genomics*: Data Science applications also enable 
an advanced level of treatment personalization through research 
in genetics and genomics. The goal is to understand the impact 
of the DNA on our health and find individual biological 
connections between genetics, diseases, and drug response. 
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 the 
human DNA. The advanced genetic risk prediction will be a major step towards more individual care.  
Page 3


 
 
Data Sciences 
Introduction 
As we have discussed earlier in class 9, Artificial Intelligence is a technology which completely depends 
on data. It is the 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: 
 
Each domain has its own type of data which gets fed into the machine and hence has its own way of 
working around it. Talking about Data Sciences, it is a concept to unify statistics, data analysis, machine 
learning and their related methods in order to understand and analyse actual phenomena with data. 
It employs techniques and theories drawn from many fields within the context of Mathematics, 
Statistics, Computer Science, and Information Science. 
Now before we get into the concepts of Data Sciences, let us experience this domain with the help of 
the following game: 
 
* Rock, Paper & Scissors: https://www.afiniti.com/corporate/rock-paper-
scissors 
 
Go to this link and try to play the game of Rock, Paper Scissors against an AI model. The challenge here 
is to win 20 games against AI before AI wins them against you. 
Did you manage to win? 
__________________________________________________________________________________
__________________________________________________________________________________ 
  
Data
• Data Sciences
• Working around numeric and alpha-numeric data.
CV
• Computer Vision
• Working around image and visual data.
NLP
• Natural Language Processing
• Working around textual and speech-based data.
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
What was the strategy that you applied to win this game against the AI machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Was it different playing Rock, Paper & Scissors with an AI machine as compared to a human? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach was the machine following while playing against you? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Data Sciences 
Data Science is not a new field. Data Sciences majorly work around analysing the data and when it 
comes to AI, the analysis helps in making the machine intelligent enough to perform tasks by itself. 
There exist various applications of Data Science in today’s world. Some of them are: 
Fraud and Risk Detection*: The earliest applications of data 
science were in Finance. Companies were fed up of bad debts and 
losses every year. However, they had a lot of data which use to get 
collected during the initial paperwork while sanctioning loans. They 
decided to bring in data scientists in order to rescue them from 
losses. 
Over the years, banking companies learned to divide and conquer 
data via customer profiling, past expenditures, and other essential 
variables to analyse the probabilities of risk and default. Moreover, 
it also helped them to push their banking products based on 
customer’s purchasing power. 
 
Genetics & Genomics*: Data Science applications also enable 
an advanced level of treatment personalization through research 
in genetics and genomics. The goal is to understand the impact 
of the DNA on our health and find individual biological 
connections between genetics, diseases, and drug response. 
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 the 
human DNA. The advanced genetic risk prediction will be a major step towards more individual care.  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
Internet Search*: When we talk about search engines, we think 
‘Google’. Right? But there are many other search engines like 
Yahoo, Bing, Ask, AOL, and so on. All these 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 
banilrs 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. 
Airline Route Planning*: The Airline 
Industry across the world is known to 
bear heavy losses. Except for a few airline 
service providers, companies are 
struggling to maintain their occupancy 
ratio and operating profits. With high rise 
in air-fuel prices and the need to offer 
heavy discounts to customers, the 
situation has got worse. It wasn’t long 
before airline companies started using 
Data Science to identify the strategic areas of improvements. Now, while using Data Science, the 
airline companies can: 
  
Page 4


 
 
Data Sciences 
Introduction 
As we have discussed earlier in class 9, Artificial Intelligence is a technology which completely depends 
on data. It is the 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: 
 
Each domain has its own type of data which gets fed into the machine and hence has its own way of 
working around it. Talking about Data Sciences, it is a concept to unify statistics, data analysis, machine 
learning and their related methods in order to understand and analyse actual phenomena with data. 
It employs techniques and theories drawn from many fields within the context of Mathematics, 
Statistics, Computer Science, and Information Science. 
Now before we get into the concepts of Data Sciences, let us experience this domain with the help of 
the following game: 
 
* Rock, Paper & Scissors: https://www.afiniti.com/corporate/rock-paper-
scissors 
 
Go to this link and try to play the game of Rock, Paper Scissors against an AI model. The challenge here 
is to win 20 games against AI before AI wins them against you. 
Did you manage to win? 
__________________________________________________________________________________
__________________________________________________________________________________ 
  
Data
• Data Sciences
• Working around numeric and alpha-numeric data.
CV
• Computer Vision
• Working around image and visual data.
NLP
• Natural Language Processing
• Working around textual and speech-based data.
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
What was the strategy that you applied to win this game against the AI machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Was it different playing Rock, Paper & Scissors with an AI machine as compared to a human? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach was the machine following while playing against you? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Data Sciences 
Data Science is not a new field. Data Sciences majorly work around analysing the data and when it 
comes to AI, the analysis helps in making the machine intelligent enough to perform tasks by itself. 
There exist various applications of Data Science in today’s world. Some of them are: 
Fraud and Risk Detection*: The earliest applications of data 
science were in Finance. Companies were fed up of bad debts and 
losses every year. However, they had a lot of data which use to get 
collected during the initial paperwork while sanctioning loans. They 
decided to bring in data scientists in order to rescue them from 
losses. 
Over the years, banking companies learned to divide and conquer 
data via customer profiling, past expenditures, and other essential 
variables to analyse the probabilities of risk and default. Moreover, 
it also helped them to push their banking products based on 
customer’s purchasing power. 
 
Genetics & Genomics*: Data Science applications also enable 
an advanced level of treatment personalization through research 
in genetics and genomics. The goal is to understand the impact 
of the DNA on our health and find individual biological 
connections between genetics, diseases, and drug response. 
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 the 
human DNA. The advanced genetic risk prediction will be a major step towards more individual care.  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
Internet Search*: When we talk about search engines, we think 
‘Google’. Right? But there are many other search engines like 
Yahoo, Bing, Ask, AOL, and so on. All these 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 
banilrs 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. 
Airline Route Planning*: The Airline 
Industry across the world is known to 
bear heavy losses. Except for a few airline 
service providers, companies are 
struggling to maintain their occupancy 
ratio and operating profits. With high rise 
in air-fuel prices and the need to offer 
heavy discounts to customers, the 
situation has got worse. It wasn’t long 
before airline companies started using 
Data Science to identify the strategic areas of improvements. Now, while using Data Science, the 
airline companies can: 
  
 
 
• Predict flight delay 
• Decide which class of airplanes to buy 
• Whether to directly land at the destination or take a halt in between (For example, A flight 
can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt 
in any country.) 
• Effectively drive customer loyalty programs  
Getting Started 
Data Sciences is a combination of Python and Mathematical concepts like Statistics, Data Analysis, 
probability, etc. Concepts of Data Science can be used in developing applications around AI as it gives 
a strong base for data analysis in Python. 
Revisiting AI Project Cycle 
But, before we get deeper into data analysis, let us recall how Data Sciences can be leveraged to solve 
some of the pressing problems around us. For this, let us understand the AI project cycle framework 
around Data Sciences with the help of an example. 
Do you remember the AI Project Cycle? 
Fill in all the stages of the cycle here: 
 
Page 5


 
 
Data Sciences 
Introduction 
As we have discussed earlier in class 9, Artificial Intelligence is a technology which completely depends 
on data. It is the 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: 
 
Each domain has its own type of data which gets fed into the machine and hence has its own way of 
working around it. Talking about Data Sciences, it is a concept to unify statistics, data analysis, machine 
learning and their related methods in order to understand and analyse actual phenomena with data. 
It employs techniques and theories drawn from many fields within the context of Mathematics, 
Statistics, Computer Science, and Information Science. 
Now before we get into the concepts of Data Sciences, let us experience this domain with the help of 
the following game: 
 
* Rock, Paper & Scissors: https://www.afiniti.com/corporate/rock-paper-
scissors 
 
Go to this link and try to play the game of Rock, Paper Scissors against an AI model. The challenge here 
is to win 20 games against AI before AI wins them against you. 
Did you manage to win? 
__________________________________________________________________________________
__________________________________________________________________________________ 
  
Data
• Data Sciences
• Working around numeric and alpha-numeric data.
CV
• Computer Vision
• Working around image and visual data.
NLP
• Natural Language Processing
• Working around textual and speech-based data.
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
What was the strategy that you applied to win this game against the AI machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Was it different playing Rock, Paper & Scissors with an AI machine as compared to a human? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach was the machine following while playing against you? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Data Sciences 
Data Science is not a new field. Data Sciences majorly work around analysing the data and when it 
comes to AI, the analysis helps in making the machine intelligent enough to perform tasks by itself. 
There exist various applications of Data Science in today’s world. Some of them are: 
Fraud and Risk Detection*: The earliest applications of data 
science were in Finance. Companies were fed up of bad debts and 
losses every year. However, they had a lot of data which use to get 
collected during the initial paperwork while sanctioning loans. They 
decided to bring in data scientists in order to rescue them from 
losses. 
Over the years, banking companies learned to divide and conquer 
data via customer profiling, past expenditures, and other essential 
variables to analyse the probabilities of risk and default. Moreover, 
it also helped them to push their banking products based on 
customer’s purchasing power. 
 
Genetics & Genomics*: Data Science applications also enable 
an advanced level of treatment personalization through research 
in genetics and genomics. The goal is to understand the impact 
of the DNA on our health and find individual biological 
connections between genetics, diseases, and drug response. 
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 the 
human DNA. The advanced genetic risk prediction will be a major step towards more individual care.  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
Internet Search*: When we talk about search engines, we think 
‘Google’. Right? But there are many other search engines like 
Yahoo, Bing, Ask, AOL, and so on. All these 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 
banilrs 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. 
Airline Route Planning*: The Airline 
Industry across the world is known to 
bear heavy losses. Except for a few airline 
service providers, companies are 
struggling to maintain their occupancy 
ratio and operating profits. With high rise 
in air-fuel prices and the need to offer 
heavy discounts to customers, the 
situation has got worse. It wasn’t long 
before airline companies started using 
Data Science to identify the strategic areas of improvements. Now, while using Data Science, the 
airline companies can: 
  
 
 
• Predict flight delay 
• Decide which class of airplanes to buy 
• Whether to directly land at the destination or take a halt in between (For example, A flight 
can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt 
in any country.) 
• Effectively drive customer loyalty programs  
Getting Started 
Data Sciences is a combination of Python and Mathematical concepts like Statistics, Data Analysis, 
probability, etc. Concepts of Data Science can be used in developing applications around AI as it gives 
a strong base for data analysis in Python. 
Revisiting AI Project Cycle 
But, before we get deeper into data analysis, let us recall how Data Sciences can be leveraged to solve 
some of the pressing problems around us. For this, let us understand the AI project cycle framework 
around Data Sciences with the help of an example. 
Do you remember the AI Project Cycle? 
Fill in all the stages of the cycle here: 
 
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
  
The Scenario* 
 
Humans are social animals. We tend to organise and/or participate in various kinds of social gatherings 
all the time. We love eating out with friends and family because of which we can find restaurants 
almost everywhere and out of these, many of the restaurants arrange for buffets to offer a variety of 
food items to their customers. Be it small shops or big outlets, every restaurant prepares food in bulk 
as they expect a good crowd to come and enjoy their food. But in most cases, after the day ends, a lot 
of food is left which becomes unusable for the restaurant as they do not wish to serve stale food to 
their customers the next day. So, every day, they prepare food in large quantities keeping in mind the 
probable number of customers walking into their outlet. But if the expectations are not met, a good 
amount of food gets wasted which eventually becomes a loss for the restaurant as they either have 
to dump it or give it to hungry people for free. And if this daily loss is taken into account for a year, it 
becomes quite a big amount. 
Problem Scoping 
Now that we have understood the scenario well, let us take a deeper look into the problem to find out 
more about various factors around it. Let us fill up the 4Ws problem canvas to find out. 
Who Canvas – Who is having the problem? 
Who are the 
stakeholders? 
o Restaurants offering buffets 
o Restaurant Chefs 
What do we 
know about 
them? 
o Restaurants cook food in bulk every day for their buffets to meet their 
customer needs. 
o They estimate the number of customers that would walk into their 
restaurant every day.