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Big Data Vs Data Science Vs Data Analytics

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Data today is growing faster than ever before which makes it important for us to know the basics of the domain like data science, big data, and data analytics. So, most people are being confused amongst these terms.

This article, 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 and responsibilities to become professionals in the field with their skills and the salary prospects in each field and then

we’ll take the example of Amazon to see their respective job responsibilities. So, let’s begin with understanding the basic concepts of these. So big data is huge volumes of data that can be structured, semi-structured and unstructured, and they are generated in multi terabytes through various digital channels like mobile, internet, and social media, etc, and these are not able to be processed using traditional applications. So now unlike traditional technologies like RDBMS, Big Data processes large volumes of data at a faster pace and also provides you an opportunity to store the data with different tools, technologies, and methodology.

Now Big Data solutions actually, provide the techniques to capture, store and analyze even search the data in seconds that make it easy to find insights and relationships for innovation and competitive games.

So with suitable analytics, big data can be used to determine the causes of business failure, cost reduction, time-saving, better decision making, and new product creation. So individuals with knowledge of Big Data is referred to as Big Data Specialist and hence Big Data Specialist will have expertise in let’s say Hadoop, Mapreduce, Spark, NO SQL, and DB tools like HBase, Cassandra, and MongoDB, etc so data science tackles big data to extract information.

It’s a field that is embracing all that is associated with structured and unstructured data starting with preparing, cleansing, analyzing, and deriving useful insights and again it’s a combination of mathematics, statistics, intelligent data capture programming, etc so in a nutshell it’s a combination of several techniques and processes working on big churns of data to gain knowledgeable business insights.

They would initially gather data sets from distinct disciplines and then compile them and after compilation, they apply predictive analysis, machine learning, and sentiment analysis. Finally, data scientists would extract some useful information from it. Now data scientists understand data in a business view and provide accurate predictions and charges for the same and thus preventing a business person from future loss. So data scientists will have expertise in let’s say statistics, logistics and linear regression differential, and integral calculus among other 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 and data analytics are similar which is not the case. Yes, they both differ at some minute point and that can be noticed through deep concentration.

Now data analytics is the fundamental level of data science and you need to know this so data analytics makes use of data mining and techniques and tools to discover patterns in the analyzed data set. So here we are mainly looking into the historical data from a completely modern perspective and applying methodologies to find a better solution. Now not only this but data analytics would also predict the upcoming opportunities that the company can exploit. So data science utilizes data analytics to provide strategic and actionable insights.

Here data analyst plays a major role. So he’ll have expertise in let’s say R statistical computing, data mining techniques, data visualization, and python programming. Now we look at some of the applications of each. So the retail industry also uses big data to remain in the retail business and stay competitive.

So the important key here is to understand and solve the customer better. So this would require proper analysis of all the sources of different data just like data from customer transactions, web locks loyalty program data, social media data, and so on and this can be easily done with big data. Now we all know that telecommunication service providers have priorities of retaining customers, gaining new ones, and expanding the current customer base. Now so to do this the act of combining and analyzing terms of customer and machine-generated data created daily can be done with big data.

Even big financial service providing forms just like retail banks, credit card companies, insurance firms, venture funds, etc, they also make use of big data for their financial services.

So the major challenge experienced by all of them is the large amount of multi-structured data embedded in multiple different systems and now this can only be taken care of by Big Data. So big data is used in various ways such as fraud analytics, customer analytics, operational analytics, and compliance analytics. Now while data science has its heights one of the most common applications in recommender systems. Yes, so this system adds so much to the user experience and also makes it easy for users to find relevant recommendations and choices of their interest.

It can be anything like relevant job postings, movies of interest, suggested videos, Facebook friends or people who bought this also bought this, etc. So several companies are using this recommender system for promoting their suggestions and products according to the user’s interest and relevance of information and demands. So recommendations always depend upon the previous search result of users.

Now another one is an internet search. So here many search engines use data science algorithms to deliver the best results in just a split second. And then the whole digital marketing ecosystem makes use of data science algorithms and that is the major reason why digital ads get higher CTR than 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, e-commerce, finance, telecom, etc now on the other hand data analytics for healthcare. Let’s check it out. So the major challenge today hospitals are facing is the cost pressure that needs to be overcome to treat their patients effectively and here machine and instrument data are used increasingly for tracking and optimizing treatment.

Then in the terms of gaming. So the advantage analytics plays a major role over here including the collection of data to optimize and spend across games. companies that are developing these games get a good insight into likes, dislikes, and relationships with their users. And then let’s suppose the travel industry.

Data analytics can optimize the buying experience through mobile and social media. Travel sites can gain insights into the customer’s desires and preferences. products can be up-sold by correlating the current sales to the subsequent increase in browsing habits and then personalized travel recommendations can also be delivered by data analytics based on social media data.

 Now let us look at some of the important roles and responsibilities in each area so a big data specialist is a professional who ensures uninterrupted flow of data between servers and applications so they work on implementing conflicts big data projects with the focus on collecting passing managing to analyze and visualizing larger sets of data to turn information into insights right.

They are actually or they should be able to decide on the needed hardware and software designs as well now the big data engineer should be able to prototypes and proof of concepts for the selected solutions bred as a data scientist as a professional who uses their technical and analytical capabilities to extract meaningful insight from data.

They would understand data from a business point of view and it also been in charge of making predictions to help businesses make accurate decisions so data scientists come with a solid foundation of Computer Applications modeling statistics and math.

They are again efficient in picking the right problems which will add again value to the organization after resolving it and then if I talk about DTI analysts then they also play a major role in data science so they perform a variety of tasks related to collecting organizing data and obtaining statistical information out of them.

They’re also responsible to present the data in form of charts graphs and tables and then use the same to build relational databases for the organization now we look at some of the skill sets that are required to be a professional in this area. if you plan or if you are planning to be professional and maintain town then you should have a mathematics and statistical skills.

For all areas of data which includes big data data science and analytics, all this is where the job begins right. Then you also need to have analytical skills so that is the ability to make meaning out of tons of data and then computers are the engines that power everyday data strategy and hence computer science.

computer sense skill is the most important for a big data professional and you also need to be able to creatively put new methods together for gathering interpreting and analyzing data. if you want to be a data scientist then you must be able to work with unstructured data which is very important and irrespective of a wedge comes from I mean whether it’s from audio social media or video feeds and then you should also need to have good knowledge of Hadoop platform and with that.

it is also an added advantage if you know coding and byte because fighting is known to be the most common coding language used in data science apart from Perl Java C C++ etc.

Now you can also have deep knowledge of ourselves because our programming is another preferable programming language and data science and let me tell you guys that although Hadoop and no SQL are major parts of data science but again knowing how to write and execute complex queries in SQL is again preferable and then you’ll need to know business skills to get a good understanding of various business objectives that push the business to grow along with its profit.

 if you want to become a data analyst then you need to have a very good knowledge of programming languages such as Python and art because they are important in this field and then as an aspiring data analyst statistical skills and mathematics as they much needed yes and again to be a data analyst.

you need to map out and convert raw data into another format that will make it more convenient for consumption and then with good communication and data visualization skills are again a must required and you must have data intuition.

 This means you need to think and reason like a data analyst so these were sort of prerequisites that you actually should have if you want to build your career into these respective domains and then devote profiles of all the three are entirely different yes which makes their salaries vary from one another as well so let’s discuss that now.

Data science is booming like anything and that is why it makes data science stand up at the top when it comes to a salary that is around one hundred and twenty-two thousand dollars per year.

The big data specialists who can earn around one hundred and fifteen thousand dollars per year followed by the data analyst with an annual income of ninety-two thousand dollars per year. now we have come to a point where we are going to discuss an example of Amazon to understand how each of them are related and providing its benefits so let’s begin with big data.

A huge amount of unstructured data is being generated from various sources now which is difficult to process through traditional databases right so due to this a Big Data profession creates an environment using various big data ecosystem tools to store and process data effectively and timely now let’s see what is the role of data scientists in Amazon example.

Here we are going to talk about how Amazon optimizes its business using data science so data scientist is the one will be able to drive sales with intentions product recommendations and then he’ll also predict the future revenue that each customer will bring to your business in a given period and also they would predict how often they are likely to make a purchase and the average value of each purchase with customer lifetime value modeling.

 They would also discover which customers are likely to churn that is to say acquiring new customers as well as maintaining relationships with existing ones the data scientist usually creates a model to automatically extract useful information from reviews and with this information amazon can efficiently maximize user satisfaction by prioritizing product updates that will have the greatest positive impact we’ll see what’s the rule of data analysts in the Amazon example.

Data analyst is responsible for supply chain management which includes managing data for products right from warehouse to the customer so Amazon also uses data extensively to manage inventory also helps to optimize transportation and pricing of delivery now data analysts will also be involved in user experience analytics mainly includes how is product search across the portfolio.

Vote decides the ranking order of products for a particularSearch or what is the best landing page for a customer coming from a Facebook Lindy diner list is also responsible for let’s say identifying merchant customer fraud detection so this is how Amazon leverages data science big data.

Data analytics to make the customer experience a more delightful one now that phenol the difference between the three so which one do you think is the most suitable for you where the option is for you you can simply decide whether you can make your current in data science or big data or data analytics, The entire batch here has thousands of data science big data and data analytics course online including our integrated program in big data and data science…

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