5V's of Big Data

5V's of Big Data:

        For me V is for Victory.In order to get success in analysis of big data we should have to know that 5V's in the Data Science.

           Hence these are the 5Vs in a Data Science let us see an elaboration of this 5V.

Volume:

       Coming to the data science what is the need of data science? The main usage of Data Science is to handle large amount of data.So coming to the concept, How much Data we are using? This is measured by the Volume.Hence Volume plays the first role in the world of Data Science.

Velocity:

       So we are having particular amount of data but in the field of data science there is a frequent incoming of data. Hence to manage those data we have to know the term velocity.So the term velocity was useful in measuring the speed of incoming data that needs to be updated before data mining.

Variety:

       So as of I previously said what data is? Its a raw fact.Now a days we are processing different kinds of data.Such as audio,video and text.Hence for data mining or processing we have to know what kind of data it should be? 

        Based on the kinds data is classified into three different categories.

Structured data         : That data that was having labels example-text

Unstructured data     : The data that was not readable example-audio,video.

Semi-Structured data: Best example of this is a log file.

Veracity:

     After the collection of data we all need to remember a term, that is what we are calling it as accuracy. We are processing the data to get some meaningful information in a database. Suppose if the data we are processing is entirely wrong then our entire analysis on the data should be wasted.In order to avoid such awkwardness we need Veracity in Data Science.Though it was not an important V in Data science we have to maintain the accuracy for our data.

Value:

     We can't get anything on analyzing the useless data.If we are using a data for analysis it definitely should have some value in the field of data science.Hence concluding that we need some value for the data.  

            

 

Data Science

 

Why Data Science?

     To know why Data Science? let us take the example of Amazon You like to buy a product in Amazon But you feel that it was too expensive. But you often visits the page and Check for price of that particular product. 

 

Why I am seeing the ads of Amazon in my Facebook newsfeed That too what I searched in amazon!!😠😠😠😠

   Amazon has its marketing strategy called promotional marketing. 

 

Hey what is promotional marketing???

     Promotional marketing is the use of any special offer intended to raise a customer's interest and influence a purchase, and to make a particular product or company stand out among its competitors. 

 

Still you don't got it???😤😤😤😤

     They get how much time you see his ad,If they decided to make you customer they do email marketing or promotional marketing or ads that we seen like facebook. Now let us see a short about Data Science and Data Scientist. 

 

What Data Science will be?

   Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured Datas.

     Today, more and more organizations are opening up their doors to big data and unlocking its power—increasing the value of a data scientist who knows how to tease actionable insights out of gigabytes of data.

 

Who is Data Scientist? 

      A person employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making. "Silicon Valley technology companies are hiring data scientists to help them glean insights from the terabytes of data that they collect everyday" 

 

So anyone can become a Data Scientist😊😊??

      My answer to that question is definitely yes ,If you have the following skills

• Math & Statistics
• Programming and Database
• Domain Knowledge and soft skills
• Communication and Visualization

   
    
     If you already have those skills CONGRATULATIONS apply for the post of Data analyst ,Data Scientist,Data Steward or Data Engineer in any company we can see the difference in the upcoming posts.