Data Science vs Machine Learning | DS vs ML | Intellipaat
Driving any data-driven business to success is possible by making use of concepts such as data science and machine learning many companies from the startups to the fortune 500 like Apple Microsoft Google and many others go on to use these concepts on a daily basis for their needs and in this session we're gonna compare data science and machine learning head-on well before we begin with the session make sure to subscribe to the Intellipaat's YouTube channel and hit the bell icon so that you never miss an update from us.
Here's the agenda for today we'll have a quick introduction to what data science actually is and understand data science basically and after this we're gonna have an introduction to machine learning and see where machine learning is used and much much more and after this we're gonna have an in-depth comparison considering a lot of factors about data science and machine learning and see how both of it holds its ground together as well and after this guys if you have any queries make sure to head down to the comments section and do let us know and we'll be happy to help you out and in case if you guys are looking for end-to-end Co certification in data science Intellipaat provides the data science architects master's program where you can learn all of these concepts thoroughly and earn a certificate in the same as well so without further ado let's begin the class coming to the first point on the agenda it is introduction to data science we need to understand what data science is guys here's a quick overview on it so basically data science is nothing but a multidisciplinary field or focus on the multidisciplinary word basically which helps in finding actionable insights from large sets of raw data and structured and unstructured data at the same time so when you talk about data science is basically you know takes an unruly entity a raw entity such as data and converts it into something valuable something useful for example information so this process of converting data into information and doing it in the very efficient way is basically one of the goals of data science and you know the main goal if you have to talk about a data scientist perspective is to basically ask questions on the data to pretty much you know find out where all the data can be studied from basically potential avenues of study and pretty much you know get answers to specific questions and ensure that you know that you know when and where to ask the right question and to ask the perfect question which fits that scenario of regarding whatever data aspects we're talking about and this skill of asking the right question this skill of or you know finding out something about your data which might not be present for the naked eye is one of the biggest goals one of the biggest skills in fact to have as a data scientist so if you have to talk about data data is considered to be the new oil because we all know how much worth oil is right so data it's pretty much is the biggest component in today's world of technology and this is because everyone understood the potential of data everyone understood what data can actually pretty much drive for their businesses driving them to success is one of the biggest aspect of it getting good insights predicting the future and performing analytics and much much more and at the end of the day this will of course you know increase profitability by somehow for all these companies which have been using and that that could be one of the main reasons why data is in the spotlight today and to quickly talk about the impact of data science we've seen companies such as Apple and we have Tesla so Tesla has the self-driving autonomous cars and Apple has Siri Siri has a chatbot and when when you go on to learn more about these concepts or when you want to practically use them you understand that these concepts are such beautiful innovations of today's world they make use of concepts such as machine learning artificial intelligence and all of these comes in fact under the umbrella of data science as well and I read about the practical use case of detection of breast cancer which happened as an experiment in 2019 oh where a lot of researchers actually are detected about a 50% chance of predicting premature breast cancer and trust me 50% is a huge number when you have to talk about anything in the field of medicine now let's talk about an airline for example Southwest Airlines is again a very good airline which runs throughout the United States so these guys saved hundred million dollars by basically analyzing how long their planes actually waited with their engines on before taking off and pretty much they could come down by a number of hundred million dollars that's a huge saving for an airline right then you have to talk about UPS UPS basically saved 39 million gallons of fuel by just optimizing how they deliver packages so UPS is basically a very famous package delivery service across the world and a courier service to be honest so these guys analyze the routes probably rerouted it very efficiently to understand you know how better can they produce routes how better can they actually follow along by delivering packages and they save 39 million gallons of fuel ladies and gentlemen and if you have to talk about a quick introduction to machine learning well what is machine learning is the first question you're supposed to ask well you know machine learning is an application of artificial intelligence to begin with of course because at the end of the day machine learning whenever we talk anything about machine learning it is to achieve AI on certain level it might be Weak AI it might be strong AI or whatever it is so machine learning basically you know provides computers the capability to learn on their own and to improve or with the experience that they've been using to learn and all of this is done without basically programming them just a quick info guys in case you guys are planning to have an end-to-end course certification in data science Intellipaat provides the data science architect master's program where you can learn all of these concepts thoroughly and earn a certificate in the same as well the link is in the description box so make sure to check it out and on that note let's get back to the session and if you have to talk about machine learning right now well right now it basically is a lot of usage of algorithms mathematics and statistics and all of these on steroids but in the future machine learning will be all about achieving artificial intelligence as I just told you even right now we are striving very close to it but here when we have to talk about cognition and achieving artificial intelligence as a whole we compare this to human level intelligence and then work with it and of course this future is very foreseeable it's very near and probably by the end of this decade we will have multiple revolutionary concepts multiple revolutionary tools and techniques which have which will basically help mankind to you know get one step closer to artificial intelligence so there are no multiple machine learning applications around as a field look so we have voice recognition we have social media we are way video surveillance malware and spam detection predictions and for and many other uses as well if you have to talk about voice recognition again voice unlock is a very famous use case of machine learning then if talk about social media or Facebook has Auto pretty much it recognizes your face automatically then Instagram knows what ads it's supposed to show you Twitter our pretty much analyzes the sentiments of the tweet Vimeo YouTube Skype all of these guys make use of machine learning on a daily basis.
If you have to talk about video surveillance think about automated traffic finds a system where you know that there doesn't have to be a cop there doesn't have to be a policeman you know finding people who are violating the traffic rules it might just be a camera which is smart enough which has a very good framework and it can pretty much capture people who are you know going against the rules and find them automatically and then if you talk about malware and spam so how does for example let me take the example of Gmail so how does Google know what mail is spam what mail is contains a malware and all what mail is it supposed to give it to your inbox so Priority Inbox as well so you know we need to talk about this again yes machine learning is being used here for a really really long time but it was so subtle pretty much that we couldn't notice and then you have to talk about predictions of the future of course there's this concept called as data analytics where we use the present data to basically analyze understand and plot a data in the future timeline so that again is a very good use case of machine learning as well if there is anything else do head to the comment sections and do let us know and of course here are some of the key moments in machine learning system in 2012 Google came out with Google brain and then deep mind deep mind basically is a brainchild of Google where it could play games at a mind-blowing level it could pretty much you know analyze millions and millions of steps you know in a game every single second as well then in 2014 we got deep face deep face was basically Facebook's brainchild and they pretty much wanted to implement deep learning for face recognition as I just told you a few sites back then of course alphago alphago is basically similar to deep mine it was part of a part of a program under google deepmind where there was a game called as go and machine was pretty much put into place where it defeated the world champion of the game go multiple times and go is considered to be the toughest board game in the entire world so it was predicting about a million times faster than the human being a million yes and this was every second so you know humans cannot think that fast every single second so this just goes on a very exponential scale with every second passing so now coming to the direct head-on between data science and machine learning the first point we'll be discussing is the meaning so data science you know as you know it is basically a field where data any any type of data structured data unstructured data semi structured data it goes through a process of being cleaned filter and basically analyzed and all of this is done to ensure there is something useful which can be put out in the other end of it and that result can be used effectively as well so that's data science coming to machine learning machine learning is actually a part of data science which makes use of multiple tools multiple techniques out there which creates beautiful algorithms so these algorithms are the basic foundation the fundamental aspect of where and how a machine can learn from data by making use of the experience then coming to second point it does this scope when if you talk about the scope again data science has a vast scope because you know if you talk about it in single dimension in today's world data science has been everywhere it has a foot which is so so strongly put in the world of data that you know every company which is driven by data use data science in one way or the other then you have to talk about machine learning machine learning is basically a part of data science as I told you and of course it is this part that it pretty much talks to it is the data modeling stage so once the data modeling stage is completed the machine learning part of data science will be done and this again is one of the very most important differences between data science and machine learning when you have to talk about the third point it has methodologies of course you know data science works with multiple manual methodologies but when you have to talk about making a machine efficient but my comparing it directly to a machine which makes use of algorithms data science lacks a little and machine learning of course cannot exist without data science and all of these data which machine learning algorithms used to work so efficiently have to come from all the other data science concepts to where you know models are basically preconditioned pre treated data cleansing is done pre-press the data preparation is done and then later these algorithms are basically applied to create a model to train these model to test if it's working to optimize it and further on coming to point number four it's the goal of these technologies so basically as you already might know it data science you know helps you to define new problems that actually need to be solved in today's world so instead of giving a direct solution to all the problems which already exist this will define new problems and these new problems have an a point of answering and this answer comes from machine learning all the techniques of machine learning all the statistical analytics methodologies and much more and of course machine learning you know it knows how the problem is sorted out and pretty much it helps in giving the solution to the problem it has all the tools that is needed all the techniques it is needed to basically generate models around the problem and to solve this same coming to point number five it's prerequisite to understand our data science you know there is a prerequisite of understanding SQL SQL a structured query language and this is needed because when you work with data you'd be talking to databases if you have to talk to databases if you have to talk to your data present in the databases you need to understand how you can create tables create databases work with your data alter your data delete your data and much more.
SQL helps just there when you talk about our machine learning machine learning requires a bit of programming in depth because languages such as a Python are Java Lisp or all of these concepts are the ones which basically implement the mathematical concepts of statistical concepts and which provide the foundation to or you know basically help the Machine understand the mathematical algorithms on which it has to use these concepts and work on point number six is the actual process so data science you know is basically a complete package it is a complete process which involves a lot of things as I told you everything from data cleansing to data analysis comes in the field of data science but when you have to talk about machine learning machine learning is just one part in this huge world of data science guys then coming to or the next point which is basically the average salary data scientists get an average salary of somewhere or 130 thousand American dollars per annum but then you have to talk about machine learning engineers machine learning engineers also get a attractive compensation of somewhere about 124 thousand American dollars per annum as well so these both are very lucrative carriers and they pay really well and they are among the top jobs in today's world then we have to talk about the companies which go on to use these technologies let's talk about data science you know everyone from unisys IBM fractal analytics we have Eva Ernst & Young Edgeworth Mu Sigma Ola cements or I could true caller and thousands and thousands of other companies basically go on to use data science for their daily needs coming to machine learning machine learning in today's world has a very strong holding in the social media industry you know everyone from Instagram LinkedIn Pinterest viber whatsapp Evernote and read of course brainchild of Google we have Twitter we chat Facebook I can you know pretty much name everything out there but then these are the main aspects where experiments were tried out a couple of years ago of using machine learning most efficiently and as per a survey conducted in 2015 pretty much machine learning was used the most efficiently in the world of social media and you know there are other companies as well as Adobe Best Buy Apple Walmart Google BBC Skype and of course thousands and thousands of other companies and all the fortune 500 companies pretty much go on to use machine learning in one way or the other to basically summarize this discussion I would like to tell you two things one of that is that data science has been rightfully called as a new oil in today's world of information because again having data is like having wealth if you understand how you can use it think about crude oil you cannot put crude oil in your vehicles as it is it has to be converted into petroleum into diesel and whatnot right so data science is just that tool which basically does exactly this when you have to talk about this analogy with respect to data then coming to machine learning of course machine learning has absolutely revolutionized the way we make use of computers the way we treat data the way we understand data and we the way we you know go on to make data work for us so these two are very lucrative careers they're the top jobs in today's world and of course they are very magical to learn to understand and of course to build a career in so on that note you've reached the end of this comparison just a quick info guys in you guys are planning to have an end-to-end course certification in data science iIntellipaat provides the data science architect master's program where you can learn all of these concepts thoroughly and earn a certificate in the same as well the link is in the description box so make sure to check it out hope you guys enjoyed this video and got to learn a lot from the same as well if you have any queries or if you have any more points you want to add to this verse this video make sure to head down to the comments section and do it now we'll be happy to help you there and reply at the earliest and on that note have a nice day.
Here's the agenda for today we'll have a quick introduction to what data science actually is and understand data science basically and after this we're gonna have an introduction to machine learning and see where machine learning is used and much much more and after this we're gonna have an in-depth comparison considering a lot of factors about data science and machine learning and see how both of it holds its ground together as well and after this guys if you have any queries make sure to head down to the comments section and do let us know and we'll be happy to help you out and in case if you guys are looking for end-to-end Co certification in data science Intellipaat provides the data science architects master's program where you can learn all of these concepts thoroughly and earn a certificate in the same as well so without further ado let's begin the class coming to the first point on the agenda it is introduction to data science we need to understand what data science is guys here's a quick overview on it so basically data science is nothing but a multidisciplinary field or focus on the multidisciplinary word basically which helps in finding actionable insights from large sets of raw data and structured and unstructured data at the same time so when you talk about data science is basically you know takes an unruly entity a raw entity such as data and converts it into something valuable something useful for example information so this process of converting data into information and doing it in the very efficient way is basically one of the goals of data science and you know the main goal if you have to talk about a data scientist perspective is to basically ask questions on the data to pretty much you know find out where all the data can be studied from basically potential avenues of study and pretty much you know get answers to specific questions and ensure that you know that you know when and where to ask the right question and to ask the perfect question which fits that scenario of regarding whatever data aspects we're talking about and this skill of asking the right question this skill of or you know finding out something about your data which might not be present for the naked eye is one of the biggest goals one of the biggest skills in fact to have as a data scientist so if you have to talk about data data is considered to be the new oil because we all know how much worth oil is right so data it's pretty much is the biggest component in today's world of technology and this is because everyone understood the potential of data everyone understood what data can actually pretty much drive for their businesses driving them to success is one of the biggest aspect of it getting good insights predicting the future and performing analytics and much much more and at the end of the day this will of course you know increase profitability by somehow for all these companies which have been using and that that could be one of the main reasons why data is in the spotlight today and to quickly talk about the impact of data science we've seen companies such as Apple and we have Tesla so Tesla has the self-driving autonomous cars and Apple has Siri Siri has a chatbot and when when you go on to learn more about these concepts or when you want to practically use them you understand that these concepts are such beautiful innovations of today's world they make use of concepts such as machine learning artificial intelligence and all of these comes in fact under the umbrella of data science as well and I read about the practical use case of detection of breast cancer which happened as an experiment in 2019 oh where a lot of researchers actually are detected about a 50% chance of predicting premature breast cancer and trust me 50% is a huge number when you have to talk about anything in the field of medicine now let's talk about an airline for example Southwest Airlines is again a very good airline which runs throughout the United States so these guys saved hundred million dollars by basically analyzing how long their planes actually waited with their engines on before taking off and pretty much they could come down by a number of hundred million dollars that's a huge saving for an airline right then you have to talk about UPS UPS basically saved 39 million gallons of fuel by just optimizing how they deliver packages so UPS is basically a very famous package delivery service across the world and a courier service to be honest so these guys analyze the routes probably rerouted it very efficiently to understand you know how better can they produce routes how better can they actually follow along by delivering packages and they save 39 million gallons of fuel ladies and gentlemen and if you have to talk about a quick introduction to machine learning well what is machine learning is the first question you're supposed to ask well you know machine learning is an application of artificial intelligence to begin with of course because at the end of the day machine learning whenever we talk anything about machine learning it is to achieve AI on certain level it might be Weak AI it might be strong AI or whatever it is so machine learning basically you know provides computers the capability to learn on their own and to improve or with the experience that they've been using to learn and all of this is done without basically programming them just a quick info guys in case you guys are planning to have an end-to-end course certification in data science Intellipaat provides the data science architect master's program where you can learn all of these concepts thoroughly and earn a certificate in the same as well the link is in the description box so make sure to check it out and on that note let's get back to the session and if you have to talk about machine learning right now well right now it basically is a lot of usage of algorithms mathematics and statistics and all of these on steroids but in the future machine learning will be all about achieving artificial intelligence as I just told you even right now we are striving very close to it but here when we have to talk about cognition and achieving artificial intelligence as a whole we compare this to human level intelligence and then work with it and of course this future is very foreseeable it's very near and probably by the end of this decade we will have multiple revolutionary concepts multiple revolutionary tools and techniques which have which will basically help mankind to you know get one step closer to artificial intelligence so there are no multiple machine learning applications around as a field look so we have voice recognition we have social media we are way video surveillance malware and spam detection predictions and for and many other uses as well if you have to talk about voice recognition again voice unlock is a very famous use case of machine learning then if talk about social media or Facebook has Auto pretty much it recognizes your face automatically then Instagram knows what ads it's supposed to show you Twitter our pretty much analyzes the sentiments of the tweet Vimeo YouTube Skype all of these guys make use of machine learning on a daily basis.
If you have to talk about video surveillance think about automated traffic finds a system where you know that there doesn't have to be a cop there doesn't have to be a policeman you know finding people who are violating the traffic rules it might just be a camera which is smart enough which has a very good framework and it can pretty much capture people who are you know going against the rules and find them automatically and then if you talk about malware and spam so how does for example let me take the example of Gmail so how does Google know what mail is spam what mail is contains a malware and all what mail is it supposed to give it to your inbox so Priority Inbox as well so you know we need to talk about this again yes machine learning is being used here for a really really long time but it was so subtle pretty much that we couldn't notice and then you have to talk about predictions of the future of course there's this concept called as data analytics where we use the present data to basically analyze understand and plot a data in the future timeline so that again is a very good use case of machine learning as well if there is anything else do head to the comment sections and do let us know and of course here are some of the key moments in machine learning system in 2012 Google came out with Google brain and then deep mind deep mind basically is a brainchild of Google where it could play games at a mind-blowing level it could pretty much you know analyze millions and millions of steps you know in a game every single second as well then in 2014 we got deep face deep face was basically Facebook's brainchild and they pretty much wanted to implement deep learning for face recognition as I just told you a few sites back then of course alphago alphago is basically similar to deep mine it was part of a part of a program under google deepmind where there was a game called as go and machine was pretty much put into place where it defeated the world champion of the game go multiple times and go is considered to be the toughest board game in the entire world so it was predicting about a million times faster than the human being a million yes and this was every second so you know humans cannot think that fast every single second so this just goes on a very exponential scale with every second passing so now coming to the direct head-on between data science and machine learning the first point we'll be discussing is the meaning so data science you know as you know it is basically a field where data any any type of data structured data unstructured data semi structured data it goes through a process of being cleaned filter and basically analyzed and all of this is done to ensure there is something useful which can be put out in the other end of it and that result can be used effectively as well so that's data science coming to machine learning machine learning is actually a part of data science which makes use of multiple tools multiple techniques out there which creates beautiful algorithms so these algorithms are the basic foundation the fundamental aspect of where and how a machine can learn from data by making use of the experience then coming to second point it does this scope when if you talk about the scope again data science has a vast scope because you know if you talk about it in single dimension in today's world data science has been everywhere it has a foot which is so so strongly put in the world of data that you know every company which is driven by data use data science in one way or the other then you have to talk about machine learning machine learning is basically a part of data science as I told you and of course it is this part that it pretty much talks to it is the data modeling stage so once the data modeling stage is completed the machine learning part of data science will be done and this again is one of the very most important differences between data science and machine learning when you have to talk about the third point it has methodologies of course you know data science works with multiple manual methodologies but when you have to talk about making a machine efficient but my comparing it directly to a machine which makes use of algorithms data science lacks a little and machine learning of course cannot exist without data science and all of these data which machine learning algorithms used to work so efficiently have to come from all the other data science concepts to where you know models are basically preconditioned pre treated data cleansing is done pre-press the data preparation is done and then later these algorithms are basically applied to create a model to train these model to test if it's working to optimize it and further on coming to point number four it's the goal of these technologies so basically as you already might know it data science you know helps you to define new problems that actually need to be solved in today's world so instead of giving a direct solution to all the problems which already exist this will define new problems and these new problems have an a point of answering and this answer comes from machine learning all the techniques of machine learning all the statistical analytics methodologies and much more and of course machine learning you know it knows how the problem is sorted out and pretty much it helps in giving the solution to the problem it has all the tools that is needed all the techniques it is needed to basically generate models around the problem and to solve this same coming to point number five it's prerequisite to understand our data science you know there is a prerequisite of understanding SQL SQL a structured query language and this is needed because when you work with data you'd be talking to databases if you have to talk to databases if you have to talk to your data present in the databases you need to understand how you can create tables create databases work with your data alter your data delete your data and much more.
SQL helps just there when you talk about our machine learning machine learning requires a bit of programming in depth because languages such as a Python are Java Lisp or all of these concepts are the ones which basically implement the mathematical concepts of statistical concepts and which provide the foundation to or you know basically help the Machine understand the mathematical algorithms on which it has to use these concepts and work on point number six is the actual process so data science you know is basically a complete package it is a complete process which involves a lot of things as I told you everything from data cleansing to data analysis comes in the field of data science but when you have to talk about machine learning machine learning is just one part in this huge world of data science guys then coming to or the next point which is basically the average salary data scientists get an average salary of somewhere or 130 thousand American dollars per annum but then you have to talk about machine learning engineers machine learning engineers also get a attractive compensation of somewhere about 124 thousand American dollars per annum as well so these both are very lucrative carriers and they pay really well and they are among the top jobs in today's world then we have to talk about the companies which go on to use these technologies let's talk about data science you know everyone from unisys IBM fractal analytics we have Eva Ernst & Young Edgeworth Mu Sigma Ola cements or I could true caller and thousands and thousands of other companies basically go on to use data science for their daily needs coming to machine learning machine learning in today's world has a very strong holding in the social media industry you know everyone from Instagram LinkedIn Pinterest viber whatsapp Evernote and read of course brainchild of Google we have Twitter we chat Facebook I can you know pretty much name everything out there but then these are the main aspects where experiments were tried out a couple of years ago of using machine learning most efficiently and as per a survey conducted in 2015 pretty much machine learning was used the most efficiently in the world of social media and you know there are other companies as well as Adobe Best Buy Apple Walmart Google BBC Skype and of course thousands and thousands of other companies and all the fortune 500 companies pretty much go on to use machine learning in one way or the other to basically summarize this discussion I would like to tell you two things one of that is that data science has been rightfully called as a new oil in today's world of information because again having data is like having wealth if you understand how you can use it think about crude oil you cannot put crude oil in your vehicles as it is it has to be converted into petroleum into diesel and whatnot right so data science is just that tool which basically does exactly this when you have to talk about this analogy with respect to data then coming to machine learning of course machine learning has absolutely revolutionized the way we make use of computers the way we treat data the way we understand data and we the way we you know go on to make data work for us so these two are very lucrative careers they're the top jobs in today's world and of course they are very magical to learn to understand and of course to build a career in so on that note you've reached the end of this comparison just a quick info guys in you guys are planning to have an end-to-end course certification in data science iIntellipaat provides the data science architect master's program where you can learn all of these concepts thoroughly and earn a certificate in the same as well the link is in the description box so make sure to check it out hope you guys enjoyed this video and got to learn a lot from the same as well if you have any queries or if you have any more points you want to add to this verse this video make sure to head down to the comments section and do it now we'll be happy to help you there and reply at the earliest and on that note have a nice day.