Friday, May 27, 2022

Big Data Vs Data Science Vs Data Analytics | Data Science vs Machine Learning | Intellipaat

Welcome to the world of data. So datatoday is growing faster than ever before which makes it important for us to knowthe basics of the domain like data science, big data and data analytics. Somost of the people are actually being confused amongst these terms. So in thesession I'll talk about the distinction between data science, big data and data analytics based on what is it, where it is used. You will also look at the roles andresponsibilities to become professionals in the field with their skills andthe salary prospects in each field and then we'll take the example of Amazon to seetheir respective job responsibilities. So let's begin with understanding the basicconcepts of these. So big data is huge .

Volumes of data that can be ofstructured, semi structured and unstructured and they are generated inmulti terabytes through various digital channels like mobile, internet and socialmedia etc and these are not able to be processed using traditional applications.So now unlike traditional technologies like RDBMS, Big Data actuallyprocesses large volume of data at a faster pace and also provides you anopportunity to store the data with different tools, technologies andmethodology. Now Big Data solutions actually provide the techniques tocapture, store and analyze even search the data in seconds that make it easy tofind insights and relationships for .

Innovation and competitive game. So withsuitable analytics, big data can be used to determine the causes of businessfailure, cost reduction, time-saving, better decision making and new productcreation. So individual with knowledge of Big Data are referred to as Big Data Specialist and hence Big Data Specialist will have expertise in let'ssay Hadoop, Mapreduce, Spark, NO SQL and DB tools like HBase, Cassandra and MongoDBetc so data science actually tackles big data to extract information. So it's afield which is embracing all that is associated with structured andunstructured data starting with preparing, cleansing, analyzing andderiving useful insights and again it's .

A combination of mathematics, statistics,intelligent data capture programming etc so in a nutshell it's acombination of several techniques and processes working on big churns of data togain knowledgeable business insights. So they would initially gather data setsfrom distinct disciplines and then compile it and after compilation theyapply predictive analysis, machine learning and sentiment analysis. Sofinally data scientists would actually extract some useful information from it.Now data scientists understand data in a business view and provide accurateprediction and charges for the same and thus preventing a business person fromfuture loss. So data scientists will have .

Expertise in let's say statistics, logistics and linear regression differential and integral calculus amongother mathematical techniques. Now you could also use tools like R, Python, Sas, SQL, tableau and so on. So most of us are of the opinion that both data science anddata analytics are similar which is not the case. Yes they both actually differat some minute point and that can be noticed through deep concentration. Nowdata analytics is the fundamental level of data science and you need to knowthis so data analytics makes use of data mining and techniques and tools todiscover patterns in the analyzed data set. So here we actually are mainlylooking into the historical data from a .

Completely modern perspective andapplying methodologies to find a better solution. Now not only this but dataanalytics would also predict the upcoming opportunities which company canexploit. So data science actually utilizes data analytics to providestrategic and actionable insights. So here data analyst plays a major role. Sohe'll have expertise in let's say R statistical computing, data miningtechniques, data visualization and python programming. Now we look atsome of the applications of each. So the retail industry, they also use big datato remain in the retail business and stay in competitive. So the importantkey here is to understand and solve the .

Customer better. So this would actuallyrequire proper analysis of all the sources of different data just like datafrom customer transaction, web locks loyalty program data, social media dataand so on and this can be easily done with big data. Now we all know thattelecommunication service providers have priorities of retaining customers,gaining new ones and expanding the current customer bases. Now so in orderto do this the act of combining and analyzing terms of customer and machinegenerated data created on a daily basis can be done with big data.Now even big financial service providing forms just like retail banks, credit cardcompanies, insurance firms, venture funds .

Etc, they also make use of big data fortheir financial services. So the major challenge experienced by all of them isthe large amount of multi structured data embedded in multiple differentsystems and now this can only be taken care of by Big Data. So big data isactually use in various ways such as fraud analytics, customer analytics,operational analytics and compliance analytics. Now while data science has itsown heights one of the most common application is recommender systems. Yesso these system adds so much to user experience and also make it easy forusers to find relevant recommendations and choices of their interest. Now it canbe anything like relevant job postings, .

Movies of interest, suggested videos,Facebook friends or people who bought this also bought this etc. So severalcompanies actually are using this recommender systems for promoting theirsuggestions and products according to the users interest and relevance ofinformation and demands. So recommendations always depend upon theprevious search result of users. Now another one is internet search.So here many search engines use data science algorithms to deliver the bestresults in just a split of second. And then the whole digital marketingecosystem makes use of data science algorithm and that is the major reasonwhy digital ads get higher CTR then the .

Conventional forms of advertisements.Let me tell you guys that data science applications are not limited to these.Yes it can be implemented on web development, ecommerce, finance, telecometc now on the other hand data analytics for healthcare. Let's check it out. So themajor challenge today hospital are facing is the cost pressure that needsto be overcome to treat their patients effectively and here machine andinstrument data is used increasingly for tracking and optimizing treatment. Thenin the terms of gaming. So the advantage analytics plays a major role over hereincluding collection of data in order to optimize and spend across games. Socompanies which are developing these .

Games get a good insight into likes,dislikes and the relationships with their users. And then let's supposetravel industry. So again data analytics is able to optimize the buyingexperience through the mobile and the social media. Travel sites can gaininsights into the customers desires and preferences. So products can actually beup sold by correlating the current sales to the subsequent increase in browsinghabit and then personalized travel recommendations can also be delivered bydata analytics based on social media data now let us look at some of theimportant roles and responsibilities in each area so a big data specialist is aprofessional who ensures uninterrupted .

Flow of data between servers andapplications so they actually work on implementing conflicts big data projectswith the focus on collecting passing managing analyzing and visualizinglarger sets of data to turn information into insights right so they are actuallyor they should be able to decide on the needed hardware and software designs aswell now the big data engineer should be able toprototypes and proof of concepts for the selected solutions bred as a datascientist as a professional who uses their technical and analyticalcapabilities to extract meaningful insight from data so they would actuallyunderstand data from a business point of .

View and it also been charge of makingpredictions to help businesses take accurate decisions so data scientistscome with a solid foundation of Computer Applications modeling statistics andmath so they are again efficient in picking the right problems which willadd again value to the organization after resolving it and then if I talkabout DTI analysts then they also play a major role in data science so theyperform a variety of tasks related to collecting organizing data and obtainingstatistical information out of them so they're also responsible to present thedata in form of charts graphs and tables and then use the same to buildrelational databases for the .

Organization now we look at some of theskill sets that are required to be a professional in this area so if you planor if you are planning to be a professional and maintain town then youshould have mathematics and statistical skills so that's very necessary for allareas of data which includes big data data science and into analytics afterall this is where job begins right and then you also need to have analyticalskills so that is the ability to make meaning out of tons of data and then ascomputers are the engines that power everyday data strategy and hencecomputer science or computer sense skill is the most important for a big dataprofessional and you also need to be .

Able to creatively put new methodstogether for gathering interpreting and analyzing data and after that if youwant to be a data scientist then you must be able to work with unstructureddata which is very important and irrespective of wedge comes from I meanwhether it's from audio social media or video feeds and then you should alsoneed to have good knowledge of Hadoop platform and with that it is also anadded advantage if you know coding and bytebecause fightin is known to be the most common coding language used in datascience apart from Perl Java C C++ etc now you can also have deep knowledge ofour ourselves because our programming is .

Another preferable programming languageand data science and let me tell you guys that although Hadoop and no SQL aremajor parts of data science but again knowing how to write and execute complexqueries in SQL is again preferable and then you'll need to know business skillsto get a good understanding of various business objectives which pushes thebusiness to grow along with its profit and if you want to become a data analystthen you need to have a very good knowledge of programming languages suchas Python and art because they are really important in this field and thenas an aspiring data analyst statistical skills and mathematics as they muchneeded yes and again to be a data .

Analyst you need to map out and convertraw data into another format that will make it more convenient for consumptionand then with good communication and data visualization skills again as amust required and you must have data intuition which means you need to thinkand reason like a data analyst so these were sort of prerequisites that youactually should have if you want to build your career into this respectivedomains and then devote profiles of all the three are entirely different yeswhich makes their salaries to vary from one another as well so let's discussthat now so data science is booming like anything and that is why it makes datascience to stand up at the top when it .

Comes to salary that is around onehundred and twenty two thousand dollars per year now next are the big dataspecialists who can earn around one hundred and fifteen thousand dollars peryear followed by the data analyst with an annual income of ninety two thousanddollars per year now we have come to a point where we are going to discuss anexample of Amazon to understand how each of them are related and providing itsbenefits so let's begin with big data so here thehuge amount of unstructured data is being generated from various sources nowwhich is difficult to process through traditional databases right so due tothis a Big Data profession creates an .

Environment using various big dataecosystem tools to store and process data effectively and timely now let'ssee what is the role of data scientists in Amazon example so here we are goingto talk about how Amazon optimizes its business using data science so datascientist is the one will be able to drive sales with intentions productrecommendations and then he'll also predict the future revenue that eachcustomer will bring to your business in a given period and also they wouldpredict how often they are likely to make purchase and the average value ofeach purchase with customer lifetime value modelling now they would alsodiscover which customers are likely to .

Churn that is to say acquiring newcustomers as well as maintaining relationship with existing ones the datascientist usually creates a model to automatically extract useful informationfrom reviews and with this information Amazon can efficiently maximize usersatisfaction by prioritizing product updates that will have the greatestpositive impact now we'll see what's the rule of dataanalysts in Amazon example so here data analyst is actually responsible forsupply chain management which includes managing data for products right fromwarehouse to the customer so Amazon also uses data extensively to manageinventory also it helps to optimize .

Transportation and pricing of deliverynow data analysts will also be involved in user experience analytics mainlyincludes how is product search across portfolio or vote decides the rankingorder of products for a particular search or what is the best landing pagefor a customer coming from a Facebook etc Lindy diner list is also responsiblefor let's say identifying merchant customer fraud detection so this is howAmazon leverages data science big data and data analytics to make customerexperience a more delightful one now that phenol the difference between thethree so which one do you think is the most suitable for you where the optionis for you you can simply decide whether .

You can make your current in datascience or big data or data analytics so entire batch here has thousands of datascience big data and data analytics course online including our integratedprogram in big data and data science so if you would like to become an expert indata science or big data you can check out our master certification trainingcourses which are big data data signed certification master course and datascience master course and big data architect master course and with this wecome to the end of this video I really hope that by now you must have got aclearer idea and a distinction between all these terms and you must have gotwhat is actually the suitable courier .

That can be for you so thank you so muchfriends for giving us your precious time if you have any queries feel free tocontact us anytime I hope the video was informative for you please like thevideo and if you have any doubts comment belowwe shall respond to it at the earliest don't forget to subscribe to our channelfor more such information videos you look out for other relatedreduce in our playlist for more detailed information visit our website now have agreat day and career ahead


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