Abstract

A profound disruptive digital transformation in our society and economy are taking place worldwide, with governments, private enterprises, and the public demanding quicker access to real-time quality data for better decision-making and social good, an output that often goes well beyond traditional statistical production capabilities. The life cycle of traditional statistical information is becoming increasingly shorter. The longer an end-user must wait for statistical information, the less valuable it becomes for decision-making. Data science and Machine Learning have emerged as a very strong, visible, and publicly recognized label for problem solving, using large, ever-growing datasets and new data sources. Hence, the ability to incorporate data science methodologies in the production of official statistics so as to benefit from emerging technologies such as web scrapping, data wrangling, cloud computing, machine learning algorithms etc. by national statistical organizations (NSOs) will become increasingly important in producing and disseminating official statistics in this era. However, the skills and competencies required to automate, analyze, and optimize such complex systems are often not part of the traditional skill of most national statistical offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. Thus, this paper presentation shall examine the emergence of data science, data science general over-view and what it means to official statistics