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 The Intersection of Big Data and Data Science

Introduction  

Traditional data analysis methods cannot prove useful in achieving big data techniques. As the data is unsupervised, so there is a need for proper tools and modeling techniques to get the meaningful outcome that is required by the company. Data science is a technique that scientifically applies statistical and mathematical approaches and tools for big data processing. Nowadays all the organizations are working on the data facts collected by them that have to increase the concept of big data. 

Data science is more critical than big data as it combines multiple fields and uses statistical algorithms that require more intelligent tools to handle large volumes of data. So, you can say that these two are inseparable. 

In this article, big data and data science will be discussed from different aspects. So let's get started. 

The intersection of Big Data and Data Science

There are the following differences found between big data and data science. 

1.Big data contains large volumes of data that are not handled with the help of traditional database programming. Whereas data science focuses on scientific activities and involves the techniques to process the big data. Data science is similar to data mining. Big data can be identified from its size, variety, and velocity. 

1.Big data includes all types and formats of data and can generate different data types from multiple sources. Data science is a scientific method that involves tools and techniques to process big data and get the required sight from the massive amount of data. Data science has also proved very useful for organizations to make the clear and right decision. 

Big data includes internet users, audio/video streaming, online discussion forums, and data is generated from system logs. Whereas data science applies scientific methods to get meaningful information from big data. It gathers complex patterns from big data and develops different models with the help of patterns. 

The applications of big data involve optimizing business processes, health, sports, research, security, law enforcement, improving commerce, financial services, and telecommunications. The applications of data science involve searching from the internet, utilities, image recognition, speech recognition, search recommenders, web development, and digital advertisements. 

Organizations need to understand the big data concepts and improve the deficiencies and make large volumes of data interpretable for the organizations. In this way, data science has come into the picture and makes it possible that with its tools the data can be converted into a form that can be easily understood by the organization and can be effectively deployed in making important decisions. 

It is a big challenge to use the potential of big data without applying any algorithms on the data or without using any data science tools and theory for getting impressive and reliable output. 

Big data is related to the distributed computing and technology whereas data science is opposed to it as it is focused on business strategies using different data patterns and gets the required data. 

Concluding Statement

There are some differences between data science and big data but there is a close relationship between them. Big data cannot be regarded as effective and reliable if the data science tools are not used to get the required insight.

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