The use of the term “data science” is increasingly common, as is “big data.” But what does it mean? Is there something unique about it? What skills do “data scientists” need to be productive in a world deluged by data? What are the implications for scientific inquiry?
Vasant Dhar. 2013. Data science and prediction. Commun. ACM 56, 12 (December 2013), 64–73.
We have run out of adjectives and superlatives to describe the growth trends of data. The technology revolution has brought about the need to process, store, analyze, and comprehend large volumes of diverse data in meaningful ways. However, the value of the stored data is zero unless it is acted upon. The scale of data volume and variety places new demands on organizations to quickly uncover hidden relationships and patterns. This is where data science techniques have proven to be extremely useful. They are increasingly finding their way into the everyday activities of many business and government functions, whether in identifying which customers are likely to take their business elsewhere, or mapping flu pandemic using social media signals.
Vijay Kotu, Bala Deshpande. 2019. Data Science Concepts and Practice. Morgan Kaufmann.
The Master of Science in “Data Science for Economics” (DSE) aims to provide a modern, effective educational programme for students interested in data science issues, with special focus on applications to the economic field.
The DSE programme started in 2018 and it has been re-designed in 2022 to join the emerging “LM-DATA” CUN class.
DSE strongly leverages STEM disciplines to provide a solid, coherent training on quantitative and methodological methods and tools of Information Technology (IT) as well as Statistics and Mathematics to interpret and analyze complex phenomena in the field of economy. DSE is conceived as a flexible educational programme with an important number of elective courses. Supported by the tutors, a student customizes the study plan through the choice between two alternative paths, namely “Data Science” and “Economic Data Analysis” paths, to further enhance STEM-oriented and economic-oriented competences, respectively. The external stakeholders of DSE are constituted by selected territorial companies and organizations focused on data science missions, and they are widely involved in the programme development in the form of lab and internship opportunities.
Given the multidisciplinary nature of the acquired knowledge and skills, the graduates of DSE can work in a variety of professional areas: small, medium, and large IT companies and research centers, companies and public bodies focused on big data management, R&D labs, innovative start-ups, healthcare companies, biomedical and pharmaceutical industries, economic and financial consulting firms, Public Administrations, National Statistical Institutes, National Banks.
Given their solid methodological education, the graduates of DSE can continue their academic experience in a PhD programme; possible scientific fields are Computer Science, Mathematics, Statistics, and Economics.