What is Data Science,Big Data and Data analytics?? 


Data science

Data scientists combine statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently to find patterns, along with the activities of cleansing, preparing, and aligning the data.

Dealing with unstructured and structured data, Data Science is a field that encompasses anything related to data cleansing, preparation, and analysis. Put simply, Data Science is an umbrella term for techniques used when trying to extract insights and information from data.

Big data

Big data is high-volume and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.” Big Data analytics find insights that help organizations make better business decisions.

A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data ;inundated; organizations of  all sizes on a day-to-day basis. In other words, Big Data refers to humongous volumes of data that cannot be effectively processed with traditional applications. The processing of Big Data begins with the raw data that isn’t aggregated or organized—and is most often impossible to store in the memory of a single computer.

Data Analytics

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.Data analytics is also known as data analysis.

Data analytics is primarily conducted in business-to-consumer (B2C) applications. Global organizations collect and analyze data associated with customers, business processes, market economics or practical experience. Data is categorized, stored and analyzed to study purchasing trends and patterns.Evolving data facilitates thorough decision-making.

For example, a social networking website collects data related to user preferences, community interests and segment according to specified criteria such as demographics, age or gender. Proper analysis reveals key user and customer trends and facilitates the social network's alignment of content, layout and overall strategy.


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