Data Science

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. 

Nuria Oliver 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. 

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

Data science needs data. 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’s 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.

Data science also needs science. It’s good to have the data, but if you don’t know how to use it, the data becomes digital waste. We need to know how to interpret data–large volumes of unstructured data– which are invisible and incomprehensible without science and its disciplines, particularly data-driven machine learning. 

According to our FTF experts, it must improve in 6 main areas: 

1. Computational-privacy violation

2. Biased social exclusion or discrimination 

3. Asymmetry in informational skills (there’s plenty of data, but most of it is in the hands of private actors)

4. Opacity and lack of transparency

5. Veracity

6. Ethics

FATEN algorithms

In addition, data science will, in the future, have to process data using FATEN algorithms, which take into account: – F for fairness, focusing on non-discrimination and cooperation – A for autonomy, accountability y augmented, referring to the need to preserve human sovereignty, clear accountability and models that increase –not replace– human intelligence- T for trust and transparency– E for education and beneficence– N for non-maleficence, minimizing the negative aspects and ensuring a minimum level of reliability, reproducibility and caution, always preserving people’s privacy.