This paper reviews the main streams of Data Science algorithm usage at central banks and shows their rising popularity over time. It contains an overview of use cases for macroeconomic and financial forecasting, text analysis (newspapers, social networks, and various types of reports), and other techniques based on or connected to large amounts of data. The author also pays attention to the recent achievements of the National Bank of Ukraine in this area. This study contributes to the building of the vector for research the role of Data Science for central banking.
