Estimation of supply and demand of tertiary education places in advanced digital profiles in the EU

Estimation of supply and demand of tertiary education places in advanced digital profiles in the EU Focus on Artificial Intelligence, High Performance Computing, Cybersecurity and Data Science Gómez Losada, Á. López-Cobo, M. Samoili, S. Alaveras, G. Vázquez-Prada Baillet, M. Cardona, M. Righi, R. Ziemba, L. De Prato, G. 2020 EUR 30377 EN This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. 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Contact information Address: Edificio Expo, C/ Inca Garcilaso 3, Sevilla 41092, Spain Email: [email protected] EU Science Hub https://ec.europa.eu/jrc JRC121683 EUR 30377 EN PDF ISBN 978-92-76-22281-1 ISSN 1831-9424 doi:10.2760/559530 Luxembourg: Publications Office of the European Union, 2020 © European Union, 2020 The reuse policy of the European Commission is implemented by the Commission Decision 2011/833/EU27 of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of photos or other material that is not owned by the EU27, permission must be sought directly from the copyright holders. All content © European Union, 2020, except: Cover image, Karpenko Ilia © Adobe Stock, 2019 How to cite this report: Gómez Losada, Á., López-Cobo, M., Samoili, S., Alaveras, G., Vázquez-Prada Baillet, M., Cardona, M., Righi, R., Ziemba, L., and De Prato, G., Estimation of supply and demand of tertiary education places in advanced digital profiles in the EU. Focus on Artificial Intelligence, High Performance Computing, Cybersecurity and Data Science, EUR 30377 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-22281-1, doi:10.2760/559530, JRC121683. i Contents Foreword ................................................................................................................ 1 Acknowledgements ..................................................................................................... 2 Abstract ................................................................................................................. 3 1 Introduction ......................................................................................................... 4 2 Methodology ........................................................................................................ 6 2.1 Estimation of available places and places in demand ..................................................... 6 2.2 Assumptions and considerations ............................................................................ 7 3 Available places and places in demand in the EU27, the United Kingdom and Norway ...................... 9 3.1 Global overview ............................................................................................... 9 3.2 EU27 ......................................................................................................... 10 3.3 EU27 Member States, United Kingdom and Norway ..................................................... 13 4 Conclusions........................................................................................................ 19 References ............................................................................................................ 20 List of abbreviations ................................................................................................. 21 List of boxes .......................................................................................................... 22 List of figures ......................................................................................................... 23 List of tables .......................................................................................................... 24 Annexes ............................................................................................................... 25 Annex 1. Methodology ............................................................................................ 25 Annex 2. Supplementary figures. Quartile distribution for unmet demand .................................. 28 Annex 3. Estimates of available places and places in demand ............................................... 30 1 Foreword The PREDICT project focuses on analysing the supply of Information and Communications Technologies (ICT) and of Research and Development (R&D) in ICT in Europe, in comparison with major competitors worldwide. ICTs are indeed the technologies underpinning the digital transformation of the economy and of society. This research aims at supporting the policy making process by providing evidence to analyse strengths and weaknesses of the European ICT industry and of technological take-up, in comparison with that of its most important trading partners, over a range of several years and to a significant level of detail. The PREDICT project has been producing comparable statistics and analyses on ICT industries and their R&D in Europe since 2006, covering major world competitors including 40 advanced and emerging countries – the EU27 plus United Kingdom, Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia. Examples of topics PREDICT addressed in over a decade of research activity are: the shift of the ICT industry, and ICT demand, from manufacturing to services; the rise of the ICT industry in Asia; the international geography of ICT R&D and innovation; the growing problems of the IPR system; the importance of mobile internet, as driving rationale of supply and demand; the deployment of ICT supply-side activities within all sectors of the economy. PREDICT is presently expanding by analysing techno-economic segments (TES) in the economy, describing the dynamics of their ecosystems with factual data from non-official heterogeneous sources, with the general objective of contributing to the measuring of digital transformation of the economy, and providing policy recommendations. Presently PREDICT is also supporting the work towards the first Digital Europe programme and the Digital Education Action Plan for increasing EU's international competitiveness and developing and reinforcing Europe's strategic digital capacities. PREDICT provides evidence about the availability in the EU27 Member States and six additional countries of adequate advanced digital skills in a number of IT domains. Moreover, the TES analytical approach has been applied to target artificial intelligence and map its worldwide landscape in the EC AI Watch. PREDICT is a collaboration between the Digital Economy Unit of the European Commission (EC) Joint Research Centre (JRC) and the Digital Economy and Skills Unit of the EC Communications Networks, Content and Technology (CNECT) Directorate General. 2 Acknowledgements Authors would like to acknowledge Ms Etienne Albiser from the Organisation for Economic Co-operation and Development (OECD) for her helpful clarifications regarding some methodological aspects of the B4 indicator (Education at a Glance 2018, OECD). We also thank Silvia Merisio (DG CNECT) for her useful comments that helped improve the report. Authors Gómez-Losada, Álvaro López-Cobo, Montserrat Samoili, Sofia Alaveras, Giorgos Vázquez-Prada Baillet, Miguel Cardona, Melisande Righi, Riccardo Ziemba, Lukasz De Prato, Giuditta 3 Abstract In order to investigate the extent to which the education offer of advanced digital skills in Europe matches labour market needs, this study estimates the supply and demand of university places for studies covering the technological domains of Artificial Intelligence (AI), High Performance Computing (HPC), Cybersecurity (CS) and Data Science (DS), in the EU27, United Kingdom and Norway. The difference between demand and supply of tertiary education places (Bachelor and Master or equivalent level) in the mentioned technological domains is referred in this report as unmet students’ demand of places, or unmet demand. Demanded places, available places and unmet demand are estimated for the following dimensions: (a) the tertiary education level in which this demand is observed: Bachelor and Master or equivalent programmes; (b) the programme’s scope, or depth with which education programmes address the technological domain: broad and specialised; and (c) the main fields of education where this tuition is offered: Business Administration and Law; Natural sciences and Mathematics; Information and Communication Technology (ICT); and Engineering, Manufacturing and Construction, with the remaining fields grouped together in a fifth category. From these estimations, it is concluded that the number of available places in the EU27, at Bachelor level, reaches 587,000 for studies with AI content, 106,000 places offered in HPC, 307,000 places in CS and 444,000 places offered in the domain of DS. At Master level this demand is comparatively lower, except for the DS domain, were it equals the offer at bachelor level. DS outnumbers AI in demand of places at Master level, with 602,000 and 535,000 demanded places, respectively. The unmet demand for AI, HPC, CS and DS in EU27 at MSc level is approximately 150,000, 33,000, 59,000 and 167,000 places, respectively. At BSc level, the unmet demand reaches 273,000, 53,000, 159,000 and 213,000 places, respectively. Another finding is that the unmet demand for broad academic programmes is higher than for specialised programmes of all technological domains and education levels (Bachelor and Master). Higher availability of places for AI, HPC, CS and DS domains is found for academic programmes taught in the ICT field of education, both at Bachelor and Master levels. For Bachelor studies, Germany and Finland are estimated as the countries with the highest unmet demand in AI, HPC, CS and DS, either with a broad or specialised scope. United Kingdom is the only studied country offering places for all fields of education and technological domains at Bachelor level and Master level. For Master studies, this is also found in Germany, Ireland, France and Portugal. Keywords: digital skills, higher education, education supply, education demand, artificial Intelligence, highperformance computing, cybersecurity, data science, digital transformation 4 1 Introduction The European Union (EU) has been making efforts to establish a genuine digital single market since the mid- 1990s. In the framework of the 2021-2027 multiannual financial framework, the European Commission (EC) proposed in June 2018 the Digital Europe programme, aiming to support the digital transformation of the European economy (proposal, European Commission (2018b), legislative resolution, European Parliament (2019)). Digital technologies, and the internet in particular, have increasingly been transforming our world, and the need to move from national digital markets to a single one has never been more important. Accordingly, the digital single market needs to be supported in the longer term to further advance Europe's digital transformation to the benefit of citizens and businesses. Investment in advanced digital skills is crucial for the achievement of this goal. In fact, the proposed Digital Europe programme, aimed at increasing EU27's international competitiveness and reinforcing strategic digital capacities, acknowledges that artificial intelligence (AI), high performance computing (HPC), and cybersecurity (CS) are key technologies that need to be further developed and made widely accessible. The Digital Europe programme aims at “increasing Europe's talent pool, bridging the digital divide, fostering greater professionalism taking into account gender balance”. In addition, the Digital Education Action Plan (European Commission (2018a)) includes a number of actions to support the development of digital competences in education, including specific measures on AI, CS, programming skills and entrepreneurship. The forthcoming updated Digital Education Action Plan, expected to be adopted in the third quarter of 2020, tackles digital skills in education in a comprehensive way, from basic to advanced levels, including AI, data literacy, HPC and CS. In particular, for AI it proposes the design on an EU-wide curricula on AI, the development of an AI framework for self-assessment of individuals, of ethical guidelines on AI for teachers, and of AI learning resources for schools. Also, the recent Communication “A European strategy for data” (European Commission (2020)), recognises critical shortages in big data and analytics in the EU and calls for an effort to reduce the current gap of 1 million digital specialists. The European strategy for data. foresees the establishment of a “Common European skills data space”, recognising the crucial role of people’s skills and the urgent need for education and training systems in advanced digital technologies. In summary, a shortage of advanced digital skills and insufficient capacities in key digital technologies are the two main challenges faced by the digital transformation in Europe. Indeed, the issue of skills evolution, Information and Communication Technologies (ICTs) and job demand is addressed by Cedefop 1 from different perspectives, having as part of its mandate to forecast trends in skill supply and demand for every two years since 2010. Cedefop expects a large increase in the use of ICT skills in the near future, also accompanied by a certain increase in autonomy and a reduction in routinised tasks (Cedefop, Eurofund, 2018). A previous study within the PREDICT project provided a mapping of digital skills and its education offer in AI, HPC and CS across Europe (López-Cobo et al., 2019). The aim of the study was to frame decisions on how to improve the availability of academic education and training in the AI, HPC and CS technological domains, to be ready to feed the increasing industry demand and anticipate the possible lack of suitable workforce. The study analysed the industrial activity in AI, HPC and CS, but also their existing academic offer of Bachelor's degree and Master's degree programmes or equivalent levels (abbreviated as Bachelor's or BSc, and Master's or MSc in this document, respectively). According to that study, at BSc level, approximately 46% of the former EU-28 countries 2 are estimated to offer tuition for the three domains (AI, HPC and CS) simultaneously, and considering only single domains, this estimation was 78% for AI, 53% for HPC and 82% for CS. Regarding the MSc level, 96% of countries offer tuition for the three domains, with highest coverage found for AI and CS (around 96% of EU countries offer AI and CS Master’s degrees), and lowest for HPC (89%). The study has been recently updated and extended (Righi & López-Cobo et al. (2020)). To further investigate the extent to which the education offer of advanced digital skills in Europe matches labour market needs, it is worth considering the number of students enrolled in such studies. Despite the critical demand of new ICT professionals to be incorporated in all economic sectors, public or private surveys or administrative registers have not been targeting this information gap for advanced technological domains. Consequently, the current report complements the study of Righi & López-Cobo et al. (2020), by performing an estimation of supply and demand of university places in Bachelor and Master levels for the AI, HPC, CS and Data Science (DS) technological domains, for different fields of education according to ISCED-F 2013 3 , in the 1 European Centre for the Development of Vocational Training (http://www.cedefop.europa.eu/) is a EU decentralised agency founded in 1975 and based in Greece since 1995. 2 Data refer to 2018-19, when the United Kingdom was still an EU Member State. 3 International Standard Classification of Education – Fields of Education and Training ISCED-F (2013). Available at: http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-fields-of-education-and- training-2013-detailed-field-descriptions-2015-en.pdf 5 EU27 countries, plus the United Kingdom and Norway. The fields of education selected to be analysed in detail are Business administration and law (BAL), Natural sciences, mathematics and statistics (NScMS), Information and Communication Technologies (ICT), Engineering, manufacturing and construction (EMC), and a fifth one named as “Rest” grouping the remaining fields of education (Education, Arts and humanities; Social sciences, journalism and information; Agriculture, forestry, fisheries and veterinary; Health and welfare; and Services). The selection of those fields of education was based on their representativeness in the academic offer for AI, HPC, CS and DS domains, in such way that BAL, NScMS, ICT and EMC accumulate 87% of the courses provided by universities and other higher education institutions in such technological domains (Righi & López-Cobo et al. (2020)). The estimation of availability and demand of places in this work is relevant for several reasons. Firstly, because it can assist administrations and analysts in assessing the relationship between labour market needs and skills supply of advanced digital skills. Secondly, it is useful to map the supply and demand of academic offer for AI, HPC, CS and DS, in order to determine if the current academic market could absorb more demand, in the short and long terms. Finally, it may be used to foresee possible development gaps due to the EU27's' unbalanced availability of academic offer for those four technological domains. Nevertheless, this estimation is subject to certain limitations. The main limitation lays in the fact that some of the data used for the estimations were originally created with a purpose different from the one in this report, which requires the formation of reasonable assumptions. This is the case of the proportion of accepted and rejected applicants at tertiary education, which corresponds to indicator B4 from the Education at a Glance 2018 report, Organisation for Economic Co-operation and Development (OECD), for which the availability of data is limited for EU countries. Hence, it was necessary to estimate the proportions for countries missing that information. The assumptions and limitations of this study are further elaborated in Subsection 2.2. This report is organised as follows. Section 2 presents a short description of the methodology used to estimate available places and students’ demand of places in the tertiary education, while a more detailed methodology is fully presented in Annex 1. The main results of the estimation of available places and places in demand for the AI, HPC, CS and DS domains are presented in Section 3. Finally, Section 4 outlines the main conclusions of this report. 6 2 Methodology This section outlines the process followed to estimate the number of available places and students’ demand of places in the EU27, for the AI, HPC, CS and DS technological domains, at Bachelor's and Master's or equivalent levels, in five fields of education (BAL, NScMS, ICT, EMC, and Rest). A more detailed description of this methodology is provided in Annex 1. Subsection 2.1 describes the estimation process and the main data sources that were consulted for that purpose, and Subsection 2.2 presents the assumptions and other important considerations on the estimation process, which must be taken into account to appropriately interpret the results provided by this study. 2.1 Estimation of available places and places in demand As previously mentioned, information on available or in demand places for AI, HPC, CS and DS at Bachelor's and Master's levels are not available from public or private surveys, or administrative registers. This conclusion is obtained after thoroughly analysing relevant and abundant information related to Higher Education Institutions across Europe. (e.g. European University Association, European Association for International Education, Eurostat, UNESCO, EU27's National Statistical offices, to cite a few). This motivated the need to estimate such figures using alternative sources of information. In the context of this report, the available places estimate aims to measure the maximum number of students that could have been enrolled in AI, HPC, CS or DS education programmes, at Bachelor's, Master's degrees or equivalent levels (ISCED 6 and 7, respectively, according to ISCED11 4 ), considering public or private education institutions in the EU. The available places estimate considers the number of applicants accepted and studying a given program, but also, those applicants who, although accepted, are not studying. This approach represents a simplification of the enrolling systems at higher education institutions. For instance, it oversees the waiting lists for permanent admissions in which students may remain before enrolment, and does not contemplate mobility between the categories of application status. A diagram showing the distribution of applicants according to their application status is provided in Figure 1. Regarding places in demand, this estimate counts not only the available places (calculated as applicants accepted to a program, either enrolled or not), but also applicants who were not successful in the admission process and were rejected. Thus, the estimate of places in demand is calculated as the number of rejected applicants plus the number of accepted applicants. The subtraction of available places from places in demand, or rejected applications, is referred in this report as students’ unmet demand of places. Figure 1. Distribution of applicants by application status. Source: JRC elaboration based on OECD (2018). 4 The International Standard Classification of Education (ISCED11) is the reference international classification for organising education programmes and related qualifications by levels and fields. Available at: http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf 13 Figure 6 presents the proportion of available places for AI, HPC, CS and DS in each field of education over the total number of available places in each field. At bachelor level, the ICT field of education is the one offering the highest proportion of programmes covering the technological domains under study: 27% of places offered in the ICT field include contents related to AI. This proportion is 18% for available places covering DS, 17% for CS and 5% for HPC. The fields of education of EMC and NScMS follow in proportion of places, including advanced digital content in AI, HPC, CS and DS. Master’s degrees are more prone to include advanced digital competences, since up to 46% of places offered in the field of ICT include DS in their curriculum, 45% of the places include AI, 25% cover CS and 21% cover HPC. Figure 6. Proportion of available places for AI, HPC, CS and DS over total available places by education level and field of education. EU27, 2019-20. Bachelor Master BAL: Business, administration and law, EMC: Engineering, manufacturing and construction, ICT: Information and Communication Technologies, NScMs: Natural sciences, mathematics and statistics, Rest: remaining fields. Source: JRC elaboration. 3.3 EU27 Member States, United Kingdom and Norway This subsection presents, at country level, estimates of available places, places in demand and unmet students’ demand, considering the scope and level of studies, for the four technological domains. The results of available and demanded places are presented for Bachelor studies in Figure 7, and for Master studies in Figure 8. Unmet demand is summarised in Table 4 for Bachelor level, and Table 5 for Master level. Table A3 1 in Annex 3 provides the estimates of available and demanded places at country level, by education level. Germany outstands as the country offering the highest number of bachelor places with broad content in the domains of AI (139,000 places), HPC (33,000) and CS (54,000) (Figure 7, left column in the figure and red bars). In DS, Greece is the country with the highest number of available places in broad programmes (74,000), closely followed by Germany (70,000). Romania ranks second in AI, followed by UK (also third in HPC and DS). France is the third country in number of available places in CS, and Spain ranks second in HPC. In Bachelor’s degrees specialised in the technological domains (Figure 7, right column in the figure), the countries offering most places are Poland (81,000), Germany (41,000) and the UK (22,000) for AI; Romania (5,000 places) for 14 HPC; the UK, Norway (both 18,000) and Germany (10,000) for CS; and Germany, Spain and Netherlands for DS (between 17,000 and 13,000 places offered). At Master’s level with broad content in the technological domains (Figure 8, left column), Germany, Italy, and France hold the first positions in AI, CS and DS (exchanging the order depending on the domain), and Spain ranks third, after Germany and France, in number of available places in HPC broad masters. In what regards places in specialised masters (Figure 8, right column), France is the country offering most places in AI (31,000), followed by Germany (25,000) and Italy (15,000). Romania offers 5,400 places for specialised masters in HPC, followed by France (3,200) and Germany (1,300). In CS, Germany and France almost reach 7,000 available places for specialised masters. DS has the highest number of places offered, led by France (32,000), Germany (28,000) and Romania (16,000). Figure 7. Available places and places in demand at Bachelor level in the EU27 Member States, the United Kingdom and Norway, by technological domain and scope, 2019-20. Source: JRC elaboration. 15 Figure 8. Available places and places in demand at Master level in the EU27 Member States, the United Kingdom and Norway, by technological domain and scope, 2019-20. Source: JRC elaboration. 16 The magnitude of the unmet demand in countries varies depending on the technological domain (AI, HPC, CS and DS), scope (broad and specialised) and level of studies (Bachelor and Master). Unmet demand for Bachelor studies is presented in Table 4 and the top three countries schematically next. — Unmet demand for bachelor programmes with a broad scope. For AI: Germany, Finland and Sweden; for HPC: Germany, Sweden and Finland; for CS: Sweden, Germany and Finland; and for DS: Greece, Germany and Finland. — Unmet demand for bachelor programmes with a specialised scope. For AI: Poland, Germany, and Finland; for HPC: Romania is the only country offering specialised bachelors; for CS: Norway, Sweden, Finland and Germany; and for DS: Finland, Germany and Netherlands. Table 4. Unmet demand at Bachelor level, by country, scope and technological domain (in thousands of places). EU27, Norway and United Kingdom, 2019-20. Broad Master Country AI HPC CS DS AI HPC CS DS BE Belgium 9.3 2.1 10.0 7.6 1.1 - 1.1 3.7 BG Bulgaria - - 2.3 2.9 - - - - CZ Czechia 0.7 - - - - - - 0.2 DK Denmark 0.9 1.3 0.7 0.5 3.5 - 0.2 0.6 DE Germany 58.8 14.2 23.0 29.6 17.4 - 4.1 7.2 EE Estonia - - - 0.7 1.9 - 1.4 - IE Ireland 2.5 1.9 2.1 2.4 0.5 - 0.6 0.7 EL Greece 1.9 1.9 10.3 41.1 1.9 - 1.9 - ES Spain 2.2 2.9 2.7 5.6 0.9 - 0.5 2.3 FR France 2.5 1.1 2.6 2.2 0.3 - 0.1 0.3 HR Croatia - - 10.6 - - - - - IT Italy 11.4 - 2.6 17.6 - - 3.2 3.0 CY Cyprus 0.4 - 0.2 0.1 0.1 - - - LV Latvia 1.4 - 1.0 2.1 0.6 - - - LT Lithuania 5.3 2.7 4.0 2.0 4.5 - 0.7 0.7 LU Luxembourg - - - - - - - - HU Hungary 6.4 1.1 1.1 1.6 2.8 - - - MT Malta 0.3 - - - - - - - NL Netherlands 6.6 2.0 8.2 13.4 5.7 - 0.9 5.4 AT Austria 3.4 - 2.7 1.9 1.9 - - - PL Poland 11.1 - 10.2 4.8 34.3 - - 2.8 PT Portugal 0.5 - - 0.3 - - - - RO Romania 7.7 - 1.6 1.6 0.8 0.8 - - SI Slovenia - - 0.6 - - - - - SK Slovakia 0.4 - 0.2 - - - - - FI Finland 27.8 7.8 14.1 23.0 11.2 - 4.1 14.2 SE Sweden 13.5 13.5 25.2 11.4 9.0 - 4.5 - UK United Kingdom 7.2 2.6 6.8 6.9 3.4 0.0 2.9 1.2 NO Norway 7.7 - 2.7 9.6 1.4 - 6.6 1.4 Source: JRC elaboration 17 As in the Bachelor level, the ranking of countries according to the unmet demand in Master studies is presented by the scope of the studies and technological domain (Table 5): — Unmet demand for Master studies with a broad scope. For AI: Germany, Italy and Sweden; for HPC: Germany, Sweden and Finland; for CS: Germany, Italy and Finland; for DS: Greece, Germany and Poland. — Unmet demand for Master studies with an specialised scope. For AI: Germany, Sweden and Italy; for HPC: Romania, Sweden and the United Kingdom; for CS: Germany, Finland and Sweden; for DS: Germany, Italy and Finland. Table 5. Unmet demand at Master level, by country, scope and technological domain (in thousands of places), EU27, Norway and United Kingdom, 2019-20. Broad Specialised Country AI HPC CS DS AI HPC CS DS BE Belgium 2.6 0.5 1.3 2.2 1.1 - 0.2 1.4 BG Bulgaria 0.3 0.5 0.5 0.3 - - 0.3 0.3 CZ Czechia 0.3 0.3 0.4 0.1 0.5 0.0 0.1 0.3 DK Denmark 3.9 2.1 1.7 3.6 1.3 0.1 0.2 1.5 DE Germany 25.0 8.4 12.6 22.5 10.6 0.5 2.9 11.8 EE Estonia 0.3 0.1 0.3 0.1 0.3 - 0.2 0.1 IE Ireland 0.7 0.4 0.3 0.8 0.3 0.0 0.1 0.5 EL Greece 1.4 1.3 1.8 1.6 0.5 - 0.8 0.5 ES Spain 2.8 1.1 0.9 3.3 1.1 - 0.4 1.0 FR France 2.6 1.7 1.6 4.8 2.7 0.3 0.6 2.7 HR Croatia - - 2.1 9.1 2.5 - - - IT Italy 16.0 0.8 5.9 19.1 6.4 0.3 1.3 6.1 CY Cyprus 0.4 0.1 0.3 0.5 0.1 - 0.2 0.1 LV Latvia 0.6 0.2 0.4 0.1 0.3 - 0.1 - LT Lithuania 2.2 0.2 0.5 1.9 0.2 0.2 0.1 0.5 LU Luxembourg 0.1 0.0 0.0 0.1 - - 0.0 0.0 HU Hungary 1.1 0.4 0.2 1.7 0.5 - 0.3 0.2 MT Malta 0.1 0.0 - 0.1 0.0 - 0.0 - NL Netherlands 3.4 0.3 1.4 5.5 1.1 0.1 0.3 2.2 AT Austria 2.6 1.5 2.1 6.2 1.1 - 0.3 1.4 PL Poland 7.2 1.8 2.7 13.7 3.5 - 0.3 4.6 PT Portugal 0.8 0.2 0.1 0.8 0.3 - 0.2 0.1 RO Romania 3.5 0.6 1.0 1.2 1.7 0.8 0.1 2.5 SI Slovenia - - - - - - - - SK Slovakia - 0.1 - 0.2 1.8 - 0.2 - FI Finland 7.1 3.2 3.5 8.0 6.0 0.3 2.3 5.1 SE Sweden 13.5 4.1 4.3 12.1 8.6 0.6 1.5 4.4 UK United Kingdom 1.7 0.8 1.4 2.1 0.8 0.1 0.6 1.3 NO Norway 2.1 1 0.9 2.1 1 0 0.1 0.9 Source: JRC elaboration Finally, the analysis is completed considering the distribution of available places by field of education for each technological domain and level of studies (Figure 9). These results are summarised in Table 6. The countries that present availability of places for all fields of education and technological domains are United Kingdom at Bachelor level, and Germany, Ireland, France, Portugal and the United Kingdom at Master level. Countries with availability places for tuition in AI, HPC, CD and DS in at least four out of five fields of education are at Bachelor level, Germany and Ireland, and at Master level, Denmark, Spain, Netherlands, Austria, Finland, Sweden, and Norway. The fields of education offering highest proportions of places at Bachelor level are EMC and ICT in AI, ICT and EMC in HPC, ICT and EMC in CS, and ICT and BAL in DS. At Master level: EMC and ICT in AI, ICT and EMC in HPC, ICT and BAL in CS, and BAL and ICT in DS. 18 Table 6. Average percentage of available places by technological domain, level of studies and field of education, EU27, Norway and United Kingdom, 2019-20. Bachelor Master Field of education AI HPC CS DS AI HPC CS DS Total 100% 100% 100% 100% 100% 100% 100% 100% BAL: Business, administration and law 7% 22% 16% 26% 14% 14% 23% 29% EMC: Engineering, manufacturing and construction 33% 23% 23% 13% 32% 26% 21% 17% ICT: Information and Communication Technologies 29% 33% 32% 29% 27% 41% 33% 27% NScMS: Natural sciences, mathematics and statistics 14% 5% 16% 17% 4% 7% 5% 8% Rest of fields 18% 17% 14% 15% 23% 13% 18% 19% Source: JRC elaboration. Figure 9. Percent distribution of available places at Bachelor and Master levels, by fields of education according to ISCED-F 2013 and technological domains, EU27, Norway and United Kingdom, 2019-20. BAL: Business, administration and law, EMC: Engineering, manufacturing and construction, ICT: Information and Communication Technologies, NScMs: Natural sciences, mathematics and statistics, Rest: remaining fields. Source: JRC elaboration. 19 4 Conclusions This study aims to estimate the number of available places, places in demand and unmet students’ demand of places in tertiary education for four technological domains (Artificial Intelligence (AI), High performance Computing (HPC), Cybersecurity (CS) and Data Science (DS)) in the EU27, the United Kingdom and Norway. This demand was estimated in the main fields of education (according to the ISCED-F 2013 classification) where this tuition is offered, namely, in Business, administration and law; Natural sciences, mathematics and statistics; Information and Communication Technologies; and Engineering, manufacturing and construction. A fifth group was also considered uniting the rest of fields of education. Additionally, the study also considers the scope of the academic programmes, or depth with which the technological domains are covered in the programmes (broad and specialised), for tuition in AI, HPC, CS and DS. The unmet demand was calculated as the difference between the number of places in demand and the available places in tertiary education. To that end, an estimation process considering different sources of information was ad hoc devised for this study. The main conclusions of this study are summarised as follow: — At Master level, the estimated number of unmet demand of places for AI, HPC, CS and DS in the EU27 is around 150,000, 33,000, 59,000 and 160,000, respectively. At Bachelor level, the number of students who could not access the studies are 273,000, 53,000, 159,000 and 213,000, also respectively. — The unmet demand for broad academic programmes is higher for all technological domains and levels of study (Bachelor and Master) than for specialised programmes. — Higher availability of places for AI, HPC, CS and DS is estimated in academic programmes offered in the ICT field of education, both at Bachelor and Master studies. The fields of education of Business, administration and law, and of Engineering, manufacturing and construction also offer a significant number of places in the studied technological domains. — France and Germany are the countries with highest number of places in specialised masters. France outstands as the country offering the highest number of places in specialised masters in the domain of AI, followed by Germany and Italy. Romania leads the specialised offer in HPC; Germany and France are equally important in CS, and France again offers the most places in specialised masters in DS, closely followed by Germany. — For bachelor studies, Germany and Finland are estimated as the countries with higher unmet demand in AI, HPC, CS and DS studies, either with a broad or specialised scope. Regarding Master studies it is less evident which countries lead in the unmet demand, with the exception of Germany. — United Kingdom offers places for all fields of education and technological domains at Bachelor and Master studies. For Master studies, this is also the case of Germany, Ireland, France and Portugal. 20 References Cedefop, Eurofund, 2018. Skills forecast: trends and challenges to 2030. Luxembourg: Publication Office. Cedefop reference series; No. 108. http://data.europa.eu/doi/102801/4492. Education and Training Monitor 2017. Directorate-General for Education, Youth, Sport and Culture. Luxembourg: Publications Office of the European Union. DOI:10.2766/32599 European Commission (2018a), Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on the Digital Education Action Plan, SSD2018) 12, Brussels, 17.1.2018 European Commission (2018b), Proposal for a Regulation of the European Parliament and of the Council establishing the Digital Europe programme for the period 2021-2027, COM(2018) 434 final, 6.6.2018. European Commission (2020), Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions “A European strategy for data” COM(2020) 66 final, 19.2.2020. European Parliament (2019) European Parliament legislative resolution of 17 April 2019 on the proposal for a regulation of the European Parliament and of the Council establishing the Digital Europe programme for the period 2021-2027 (COM(2018)0434 – C8-0256/2018 –2018/0227(COD)) Eurostat (2018). Students enrolled in tertiary education by education level, programme orientation, sex and field of education [educ_uoe_enrt03]. Last update: 03-07-2019 [consulted 1st August, 2019]. López-Cobo, M., De Prato, G., Alaveras, G., Righi, R., Samoili, S., Hradec, J., Ziemba, L.W., Pogorzelska, K., Cardona, M., 2019. Academic offer and demand for advanced profiles in the EU. Artificial Intelligence, High Performance Computing and Cybersecurity. JRC Technical Reports (JRC113966). Luxembourg: Publications Office of the European Union. OECD (2018), "Indicator B4 Who is expected to enter tertiary education?", in Education at a Glance 2018: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/eag-2018-17-en. Orr, D., Usher, A., Haj, C., Atherton, G., Geanta, I., 2017. Study on the Impact of admission systems on higher education outcomes. Volume I: Comparative report. Directorate General for Education and Culture. European Commission, Luxembourg: Publications Office of the European Union, 2017. Personal Communication (2019). Email from Ms Etienne Albiser (OECD), 27th June, 2019. Righi, R., López-Cobo, M., Alaveras, G., Samoili, S., Cardona, M.., Vázquez-Prada Baillet, M., Ziemba, L.W., and De Prato, G., Academic offer of advanced digital skills in 2019-20. International comparison. Focus on Artificial Intelligence, High Performance Computing, Cybersecurity and Data Science, EUR 30351 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-21541-9, doi:10.2760/225355, JRC121680. Sarkar, D., 2008. Lattice: Multivariate data visualization with R. Springer, New York. Studyportals (2020). COVID-19 report: An outlook for international student recruitment. April 2020. UNESCO Institute for Statistics, 2015, International Standard Classification of Education. Fields of education and training 2013 (ISCED-F 2013). Detailed field descriptions, Montreal, Canada, ISBN 978-92-9189-179-5, DOI http://dx.doi.org/10.15220/978-92-9189-179-5-en. 21 List of abbreviations AI Artificial Intelligence BAL Business, Administration and Law BSc Bachelor of Science CS Cybersecurity DS Data Science EMC Engineering, Manufacturing and Construction EU European Union HPC High Performance Computing ICT Information and Communication Technology MSc Master of Science NScMs Natural Sciences, Mathematics and Statistics OECD Organisation for Economic Co-operation and Development Rest Rest of fields of education UNESCO United Nations Educational, Scientific and Cultural Organization List of country codes and country names Country code Country name Country code Country name EU27 Member States BE Belgium LU Luxembourg BG Bulgaria HU Hungary CZ Czechia MT Malta DK Denmark NL Netherlands DE Germany AT Austria EE Estonia PL Poland IE Ireland PT Portugal EL Greece RO Romania ES Spain SI Slovenia FR France SK Slovakia HR Croatia FI Finland IT Italy SE Sweden CY Cyprus Non-EU27 countries LV Latvia UK United Kingdom LT Lithuania NO Norway 22 List of boxes Box A1 1. Estimation of available places and places in demand for ICT, in France and Belgium, at BSc level. ..........................................................................................................................27 29 Figure A2 2. Estimations of unmet demand (quartiles) for AI (A), HPC (B), CS (C) and DS(D), at Master or equivalent level, EU27, United Kingdom and Norway, 2019-20. Source: JRC elaboration. 30 Annex 3. Estimates of available places and places in demand Table A3 1 Estimates of available (AP) and in demand (DP) places for AI, HPC, CS and DS, by education level (in thousands). EU27 Member States, United Kingdom and Norway, 2019-20. Bachelor Master AI HPC CS DS AI HPC CS DS Country AP DP AP DP AP DP AP DP AP DP AP DP AP DP AP DP BE 24.4 34.8 4.9 7.0 26.1 37.2 26.6 37.9 8.8 12.5 1.2 1.8 3.5 5.0 8.4 12.0 BG - - - - 3.3 5.6 4.3 7.1 0.4 0.7 0.8 1.3 1.1 1.8 0.8 1.3 CZ 4.6 5.3 - - - - 1.2 1.4 4.8 5.6 2.4 2.7 3.6 4.2 2.6 3.0 DK 10.2 14.7 3.0 4.3 2.0 2.9 2.6 3.7 12.1 17.3 5.2 7.4 4.4 6.3 11.9 17.0 DE 179.6 255.8 33.4 47.6 63.9 91.0 86.8 123.7 83.7 119.3 21.0 29.9 36.7 52.2 80.9 115.3 EE 5.6 7.5 - - 4.2 5.6 2.0 2.6 1.8 2.4 0.4 0.5 1.4 1.9 0.6 0.8 IE 5.4 8.4 3.4 5.2 4.9 7.6 5.6 8.7 1.7 2.7 0.8 1.3 0.9 1.4 2.2 3.5 EL 6.7 10.4 3.4 5.2 21.8 33.9 74.0 115.1 3.5 5.4 2.3 3.6 4.7 7.3 3.9 6.0 ES 19.6 22.6 18.7 21.6 21.1 24.3 50.6 58.5 25.1 29.0 6.9 7.9 8.3 9.6 28.0 32.4 FR 32.2 34.9 13.3 14.4 32.2 34.9 28.8 31.3 61.4 66.6 22.7 24.6 24.8 27.0 88.3 95.9 HR - - - - 15.7 26.3 - - 3.7 6.2 - - 3.2 5.3 13.5 22.6 IT 26.8 38.1 - - 13.6 19.3 48.4 69.0 52.7 75.1 2.4 3.4 16.9 24.0 59.4 84.6 CY 0.6 1.1 - - 0.3 0.5 0.1 0.2 0.7 1.2 0.2 0.4 0.7 1.1 1.0 1.6 LV 3.0 5.0 - - 1.4 2.4 3.2 5.3 1.2 2.0 0.3 0.4 0.7 1.2 0.1 0.2 LT 6.9 16.7 1.9 4.6 3.3 8.1 1.9 4.6 1.6 4.0 0.3 0.7 0.4 1.1 1.7 4.1 LU - - - - - - - - 0.1 0.2 0.1 0.2 0.2 0.3 0.3 0.4 HU 18.0 27.2 2.2 3.3 2.2 3.3 3.1 4.6 3.1 4.7 0.8 1.2 1.0 1.5 3.6 5.5 MT 0.5 0.7 - - - - - - 0.1 0.2 0.0 0.1 0.0 0.1 0.1 0.2 NL 29.0 41.3 4.8 6.8 21.5 30.6 44.3 63.2 10.5 14.9 1.0 1.4 4.0 5.7 18.0 25.7 AT 12.4 17.6 - - 6.3 9.0 4.4 6.2 8.7 12.3 3.5 5.0 5.7 8.1 17.8 25.4 PL 107.1 152.6 - - 24.0 34.2 18.0 25.7 25.2 35.8 4.3 6.2 7.0 9.9 43.1 61.4 PT 4.0 4.5 - - - - 2.5 2.9 9.3 10.4 1.7 1.9 2.6 3.0 7.7 8.6 RO 55.0 63.5 5.1 5.9 10.2 11.8 10.2 11.8 33.6 38.8 9.1 10.5 7.4 8.6 23.7 27.4 SI - - - - 1.7 2.3 - - - - - - - - - - SK 2.6 3.0 - - 1.3 1.5 - - 11.6 13.4 0.6 0.6 1.1 1.3 1.1 1.3 FI 19.6 58.6 3.9 11.7 9.1 27.3 18.7 55.9 6.6 19.7 1.7 5.1 3.0 8.8 6.6 19.7 SE 13.0 35.5 7.8 21.3 17.1 46.8 6.5 17.9 12.7 34.7 2.7 7.4 3.4 9.2 9.5 26.1 UK 68.2 78.7 17.2 19.9 62.5 72.2 52.5 60.6 16.1 18.6 5.8 6.7 13.0 15.1 22.2 25.6 NO 25.3 34.4 - - 25.9 35.2 30.4 41.4 8.4 11.5 2.8 3.8 3.0 4.1 8.3 11.2 -: Not available due to missing information in source data. 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