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Data Science

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Most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. The new benefits that big data analytics brings to the table are speed and efficiency. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

The use of data analytics is in every segment of every industry: hospitality, consumer products, manufacturing, financial services, life sciences & healthcare, government, and more. Results are widely used to make more informed business decisions and by researchers and scientists to verify or disprove scientific models and hypotheses.

Data Analytics has become a common term in today’s corporate strategy, but could be divided into the following sub-domains:

Data management. Data needs to be high quality and well-governed before it can be reliably analyzed.

Data mining. Data mining technology helps to examine large amounts of data to discover patterns in the data which can be used for further analysis to help answer complex business questions.

In-memory analytics. By analyzing data from system memory instead of from a hard drive it is possible to derive immediate insights from data and act on them quickly.

Predictive analytics. Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.

Text mining. Text mining uses machine learning or natural language processing technology to analyze text – emails, Twitter feeds, surveys, competitive intelligence, etc.– to help analyze large amounts of information and discover new topics and term relationships.

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