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Data Science Certification allude to quantitative and qualitative techniques and processes that are used to intensify productivity and business gain. Data is drawn out and categorized to analyze and identify behavioral data, patterns and methods vary according to organizational requirements. It is also known as Data Analysis.
Data Analytics is predominantly conducted in business-to-consumer(B2C) applications. Data is generally classified, stored and analyzed to study patterns and trends.
International organizations collect and analyze data correlated with business processes, customers, market economics or practical experience. Data Analytics is Emerging data facilities through decision-making. If we consider an example, a social networking website collects data related to user’s preference, interests of community and it divides according to specified prototype such as demographics, gender or age. Proper data analysis reveals key user and customer trends and expedite the social network’s alignment of content, layout and strategy behind it.
Data Analytics in another way is making sense of Big Data. It is the main domain of Data Analytics. Many tools and techniques are set up in order to collect, transform, cleanse, classify, and convert data into effortlessly understandable data visualization and report formats. Data Analytics follows a series of steps which are 1) Get Data 2) Analyze it 3) Visualize it 4) Publish and 5) Consume.
Data Analytics is one of the most complex term, when it comes to big data applications. The most important attributes of big data include volume, velocity, and variety. The need for Big Data Analytics bounces from all data that is created at extremely fast speeds on the internet. It is predicted that by the end of 2020 the cumulative data that is generated every second will amount to 1.7 MB which is contributed by individuals across the globe. This displays the amount of data being generated and hence need for Big Data Analytics tools to match all that data.
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