Data Science literally looks to the future

The development of Data Science went along with. The introduction of Big Data technologies and data analysis. And although these areas often overlap. They should not be confus with each other. All of them involve understanding large amounts of information. But if data analytics answers questions about the past (for example, about changes in the behavior of customers of an. Internet service over the past few years), then  Big data DS specialists can create models that prict. What will happen tomorrow. Including pricting the demand for certain goods and services. Why Data Science is for business Companies use Data Science regardless of the size of the business, statistics show Kaggle (a professional social network for data scientists.

Data scientists cannot accurately

An according to IDC and Hitachi 78% of enterprises Romania Phone Number List confirm that the amount of information analyz and us has increas significantly in recent years. Business understands that unstructur information contains very important knowlge for the company that can affect business results, the authors of the study note. Of course,  prict the future of a company and take into account absolutely all possible risks. “All models are wrong, but some of them are useful,” said British statistician George Box ironically. However, Data Science tools serve as a good support for companies that want to make more inform and inform decisions about their future.

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Draw conclusions and prict the future

How data scientists work To work with data, data scientists use a whole range of tools – statistical modeling packages, various databases, special software. But, most importantly, they use artificial intelligence technologies and create machine BT Lists learning models (neural networks) that help businesses analyze information. Each such neural network nes to be plann, built, evaluat, deploy, and only then proce to its training. “Now, according to our estimates, in the process of working on AI solutions, only 30% of the time of specialists is spent on training models.

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