{"id":12648,"date":"2019-11-14T12:19:00","date_gmt":"2019-11-14T11:19:00","guid":{"rendered":"https:\/\/www.fundacionbankinter.org\/noticias\/ciencia-de-datos\/"},"modified":"2025-12-01T23:35:55","modified_gmt":"2025-12-01T22:35:55","slug":"ciencia-de-datos","status":"publish","type":"noticias","link":"https:\/\/www.fundacionbankinter.org\/en\/news\/ciencia-de-datos\/","title":{"rendered":"Data Science"},"content":{"rendered":"\n

Data science is a set of tools that emerged out of the intersection of math and statistics, IT, business management, corporate knowledge and other relevant sectors. <\/strong><\/p>\n\n\n\n

Nuria Oliver<\/a> says a new field within data science, computational social science, leverages large-scale, aggregated behavioral data in order to verify social science theories. For the last ten years, Oliver has also been working in an area of computational social science: data science for social good. <\/strong><\/p>\n\n\n\n

The goal of this area is to leverage the analysis and processing of massive data sets<\/strong> in order to make better decisions in fields like public health, financial inclusion, climate change or emergency relief during natural disasters. <\/p>\n\n\n\n

Data science needs data.<\/strong> Every year, we generate more data than in the previous 5,000 years combined, and the ability to interpret it is increasingly becoming an incredibly valuable asset in today\u2019s economy. According to a European Commission report, the European data economy will be worth over 700 billion next year, as long as favorable policy and legislation is implemented in time and investment in ICTs is promoted.<\/p>\n\n\n\n

Data science also needs science.<\/strong> It\u2019s good to have the data, but if you don\u2019t know how to use it, the data becomes digital waste. We need to know how to interpret data\u2013large volumes of unstructured data\u2013 which are invisible and incomprehensible without science and its disciplines, particularly data-driven machine learning. <\/p>\n\n\n\n

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