Data Science vs Artificial Intelligence
Hey everyone welcome to the blog by poruk. you might have heard of data science and artificial intelligence have been a really big boon to the world of information technologybecause this entire world is one big data problem and in this session we'regonna compare data science head-on with artificial intelligence and see where it is.
We'll begin by taking a quickintroduction to data science followed by which we can check out where datascience is actually used and after this we can check out what artificialintelligences and some facts about artificial intelligence at the same time and after this we're gonna compare data science head-on with artificialintelligence and guys if you have any queries make sure to head down to thecomment section below I do let us know and we'll be happy to help out of theearliest and guess if you're interested in doing an end to end Coursecertification in data science Intellipaat provides the data sciencearchitect master's program where you can learn all of these concepts thoroughlyand earn a certificate at the same time well without further ado let's begin the class coming to the first item on the agenda it is introduction toartificial intelligence I am sure in the past decade and in fact in this decadewe've been hearing a lot about artificial intelligence right so
what is artificial intelligence?
It is very simple guys it is a theory and the development of computer systems to basically mimic human intelligence or wecan even stay at it as you know machine performing a task which would actually require.
Some sort of human intervention there well there are many tasks whereartificial intelligence at this point of time is actually a lot better than humanbeings for example our we have image recognition we have speech recognitionprocess of decision making and translations when you think about allfour of these basically you will know that there is some sort of humanintervention required because we are trained to look at images we understandspeech and our brain is logical enough to you know perform very good decision making and decision taking steps but then when we talk about translations aswell pretty much we have been trained to under stand different languages where we can you know translate between one to the otheras well but nowadays all this can be done faster more efficiently and eventually better than human beings by making use of machines guys so whereare we with artificial intelligence today when artificial intelligence hasbeen so subtly integrated into our lives we don't even notice it is there any more guys why do we see this stick with me for the next couple of slides and you will understand just this because when you look around you there as artificial intelligence everywhere and also you might have heard of the term such asartificial intelligence is the best career path of this decade and what notand that is a heavy statement because we are just at the start of this decade butthen all the trends and all the analytics which have been performed allthe surveys which have been conducted pretty much says that artificial intelligence as a career path and the technology is on the rise and it will betrending for the next ten years and as you can see artificial intelligence hasthe biggest community ever well if something is trending and if somethingis constantly on the number one spot for the last couple of years and definitelyit attracts the attention of everyone right so that's basically that and thenguys coming to where artificial intelligence has been used in this worldwell this is why I told you it has been very subtly integrated into our liveseverything from Google assistant Cortana you know there's Siri there's bigspeeders Alexa and much more so you can talk to your phones you can talk to yourcomputers you can talk to your machines to get so much done in fact there aresmart homes being implemented for the last couple of years well all you haveto do is ask Alexa to do stuff for you you know Alexa can unlock the door foryou Alexa can turn on your TV home theaters and you know pretty much turn on the lights move away the curtains and pretty much everything sowhen you think about it it has been there around us for a while now but thenwhen we talk about the actual trending stuff about artificial intelligence youknow when we talk about revenues especially for the next five years youcan see from the graph all the way from 2016 to 2025 the amount given is inmillion dollars so you know pretty much it was three fifty seven million dollarsin 2016 basically the revenue generated by enterprise applicationsfrom 357 it has been predicted that in 2025 it's gonna hit thirty one thousandtwo hundred and thirty six million u.s. dollars. guys
So imagine the growth as you can see on your screen I am sure you guys can figure out that it is anexponential growth just a quick info in case you guys arelooking for end-to-end course certification in data science Intellipaat provides the data science architect master's program where you canlearn all of these concepts thoroughly and earn a certificate in the same thelink is given in the description box so make sure to check it out and withoutfurther ado let's get back to the class and then when we talk about how muchresearch is going on with respect to artificial intelligence here is thething in the year 2000 there was less than around one thousand one thousandtechnical papers which are actually published but then when you talk about 2015 and up there is skyrocket and in fact from 15 all the way till 25 the numbers just keep going as high up as you can think as so we started all theway from 1500 in 2015 it hit somewhere around 18,000 and now you know prettymuch it is very more than 40,000 papers which have been published guys so it is that nine times growing every single year is what we can tell and then coming to this slide.
Where we're trying to see where artificial intelligence fits inwell there are a couple of steps when we talk about data you know steps such asdata generation data storage data processing and inside so the first stepspretty much include data generation and data storage and this gets covered alltogether by the world of big data guys and then coming to data processing andactionable insights well artificial intelligence here we gobecause when we talk about data processing we can make use of machinelearning deep learning neural networks and there are many other concepts guysnatural language processing you know support vector machines and much more -pretty much process all of the data and at the end of it we have very good dataanalysis and analytics tools which make use of artificial intelligence to giveus beautiful visualizations which can be used to derive actionable insight andthen finally coming to the comparison between data science and artificialintelligence on the first point we'll be discussing is the meaning well guys didas you can see is a very detailed process which basically involves and youknow pre-processing the data performing some analysis on this data atthe end of the analysis comes the visualizations where we'll be generatinga lot of graphs a lot of visuals and at the end of it you gonna use all of thisto perform some predictions on some future trends guys but then coming toartificial intelligence artificial intelligence is basically you knowimplementing a model and what this model at the end of the day does is that youknow it is used to forecast certain future trends future events what mighthappen and how we can get there if that is the case while coming to point numbertwo it's the skills well when you talk about data science again or data scienceyou have to understand this is an umbrella term for a lot of statisticaltechniques a lot of design techniques and development methodologies guys wellwhen we talk about artificial intelligence it has got a lot to do withalgorithm design algorithm development efficiency conversions and in fact evendeployment of all of these design and developed products guys but then coming to the next point
It is the technique here is where there is a lot of difference between artificial intelligence and data science in data science you know we are actually majorly concerned about making use of dataanalysis and data analytics guys so data analysis where we'll be using pass datato analyze the present tense in a very simple term and with respect to dataanalytics we'll be using the past and the present data to predict the futuretrends guys that is why there is a small difference between analysis andanalytics but then when we talk about artificial intelligence you need to knowthat you know will be concerned with a lot of machine learning concepts in thisparticular stage it can be machine learning it can be a lot of concepts asdeep learning neural networks and much more as I just mentioned in the coupleof slides ago well basically coming to the next pointit is the knowledge well when we talk about data science data science wasactually established you know to find hidden patterns and hidden trends indata to make more sense of the data and to make it a more friendlier entity butthen we talked about artificial intelligence you need to know that youknow with respect to artificial intelligence it is to make sure whateverdata we are dealing with can be autonomously handled so we are trying toremove the human from the picture when we talk about artificial intelligenceto give the Machine some depth some understanding of the data to let it workon its own without the human dependency.
Then coming to the next point quickly is processing with respect to processing again data science does notinvolve a very high degree of scientific processing it involves a lot of complexprocedures yes but then all of these are not the highest standards of scientificprocessing guys but then when we talk about artificial intelligence even asthe name suggests it can be a bit more complex when we talk about artificialintelligence guys because your will be having a lot of high-level processing alot of complex processing to deal with because at the end of the day we aretrying to implement some sort of autonomy in the machines you know whicheventually are telling the machines that they need to step up their game and tomimic the human brain and the human brain in today's world is the mostintelligent being there is right so coming to the next one is the goal ofthese technologies well with respect to data science complex models you know arebasically built by making use of various insights various facts about the datait's a lot of statistical techniques modeling and whatnot but then we talkabout artificial intelligence well artificial intelligence was meant tobuild models that emulate cognition guys but emulate cognition what we basicallymean as it again we're trying to make the machines self sustained enough sothat where it would not require certain human dependencies and the next thing isthat it will require some sort of human understanding to a certain level becausethat is what is required to achieve some sort of cognition and then coming to thesalary of the develop words well the average salary of a data scientist isaround hundred and thirty thousand US dollars per year and the average salaryof an artificial intelligence developer is around one hundred and twentythousand US dollars per year guys this is the average amount thatI've mentioned to keep it to the scope of all the viewers well guys what youneed to understand at this point of time is that we have kept the average numberon the screen you can pretty much have access to three to four times thesalaries mentioned on your screen regardless of which country you you'reworking from or what company you're working for as well guys so you have toknow that these both carrier parts are very fun to work withvery lucrative and at the end of the day you will have a lot of fun at your jobat the same time so to summarize the differences between these two basicallyI want to say that artificial intelligence you know makes use of theseloops of perspection that we call and then pretty much we use some sort ofplanning to become intelligent in how we handle data guys but then when you talkabout data science data science is all about using patterns all about usingtrends and pretty much you know getting at a decision faster more efficientwhich might have crossed the eye when we talk about data so coming to when weshould go about using data science or artificial intelligence well datascience is actually preferred when you need to understand and find out patternsand trends in the data it is used if you have some sort of a mathematical requirement where you need an in depth and the faster analysis of the same.
It is also used when you need to perform EDA EDA is basically exploratory data analysis where you'll be hunting and pretty much going through the data tofind something which might skip the naked eye as we said and then you'llalso be using it if you need some sort of improvement which is supposed to belinear which you need a constant growth in your particular concepts and alsoit'll be required if you require very fast mathematical processing guys butthen the last requirement is that you know there are a lot of industryrequirements which will involve a lot to do with prediction for example if you'rea sales company a product company you will be concerned with what is theproducts you might sell next month next year the next decade or so right sopredictive analytics also helps your and data science does just that come into artificial intelligence.
Artificial intelligence is a requirement if you know you require some sort of precision which is out of this worldguys and I mean it when I say that because AI is made to use in full potentials in full swing when we are basically trying to get the greatest degree of precision that we can and then when we talk about decision making as well artificial intelligence is known to be faster or when we compare directly tohumans in many aspects so that is that and then coming to logical decisionhandling again guys as humans there might be emotional interference inmultiple tasks where the requirement does not call for that in that casepretty much artificial will not have any swing to any emotionsand it'll work fine and then handling repetitive tasks for humans can be a bitof a challenge but then when we talk about AI pretty much it can handle itwith ease guys again working 24/7 365 days without any breaks or performingvery good risk analysis risk-taking abilities and making sure you'reefficient through all of these points that I mentioned on a screen AI does itbetter than humans at this point of time and then there are many other pointswith respect to data science and artificial intelligence as well guyswell to keep it to the scope of all the viewers of this particular video we hadto simplify it to present it here guys now coming to the companies which makeuse of data science and artificial intelligence well we have everyone fromApple Google Amazon Twitter of Facebook Nvidia and thousands of other companieswho make use of data science on a daily basis then coming to the companies whichmake use of artificial intelligence we have everyone from Walmart labsMicrosoft Genpact Accenture Ericsson KPMG and all the fortune 500 companiesthat you can ever think of guys so in today's world where we live data as anunruly and entity already know that but worry not concepts such as data scienceand artificial intelligence are in full swingto make data to make all of these processes a friendlier entity and tohelp us work with it faster more efficiently and with better outcomes andresults just a quick info in case you guys arelooking for end-to-end course certification in data science Intellipaat provides the data science architect master's program where you canlearn all of these concepts thoroughly and earn a certificate in the same the link is given in the description box so make sure to check it outI hope this session was very informative for you all if you have any more pointsyou wanna add on this data science versus artificial intelligence.
Are Machine Learning and Data Science the same?
No, Machine Learning and Data Science are not the same. They are two different domains of technology that work on two different aspects of businesses around the world. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyse and understand trends. However, that's not to say that there isn't any overlap between the two domains. Both Machine Learning and Data Science depend on each other for various kinds of applications as data is indispensable and ML technologies are fast becoming an integral part of most industries
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