TY - GEN
T1 - Master as a Service
T2 - 7th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2019
AU - Navarro, Joan
AU - Zaballos, Agustín
AU - Fonseca, David
AU - Torres-Kompen, Ricardo
N1 - Publisher Copyright:
© 2019 ACM.
PY - 2019/10/16
Y1 - 2019/10/16
N2 - The recent and rapid growth of data-driven applications, fostered by the advent of enhanced Information and Communication Technologies (ICTs) together with the broad availability of modern high-performance storage and computing infrastructures, has created a considerable gap of experts in this new field. The quick evolution of these technologies, their dissimilarities with traditional approaches, and the broad skills set required to master them, might prevent existing professionals working in industry to gain high quality knowledge and experience in Big Data related areas. Therefore, universities and teaching professionals must propose feasible and effective alternatives to train professionals and students in these topics. The purpose of this paper is to present the Master as a Service (MaaS) approach, that is currently being used to train students in Big Data- related areas (e.g., eHealth, Digital Transformation, etc.), following a multidisciplinary, Project Based Learning strategy. More specifically, students coming from different master's degrees and undergraduate backgrounds (ranging from management studies to computer engineering, including architects, social and physical sciences) are trained to address latent and future challenges in Big Data and High-Performance Computing technologies by combining their profiles, and exposing them to real-world challenges that require the very best of each different profile. The results obtained from the implementation of the MaaS approach during the last two years in terms of both student satisfaction and employability rate, confirm the benefits of this method and encourage practitioners to keep working in this direction.
AB - The recent and rapid growth of data-driven applications, fostered by the advent of enhanced Information and Communication Technologies (ICTs) together with the broad availability of modern high-performance storage and computing infrastructures, has created a considerable gap of experts in this new field. The quick evolution of these technologies, their dissimilarities with traditional approaches, and the broad skills set required to master them, might prevent existing professionals working in industry to gain high quality knowledge and experience in Big Data related areas. Therefore, universities and teaching professionals must propose feasible and effective alternatives to train professionals and students in these topics. The purpose of this paper is to present the Master as a Service (MaaS) approach, that is currently being used to train students in Big Data- related areas (e.g., eHealth, Digital Transformation, etc.), following a multidisciplinary, Project Based Learning strategy. More specifically, students coming from different master's degrees and undergraduate backgrounds (ranging from management studies to computer engineering, including architects, social and physical sciences) are trained to address latent and future challenges in Big Data and High-Performance Computing technologies by combining their profiles, and exposing them to real-world challenges that require the very best of each different profile. The results obtained from the implementation of the MaaS approach during the last two years in terms of both student satisfaction and employability rate, confirm the benefits of this method and encourage practitioners to keep working in this direction.
KW - Big data
KW - Cloud computing
KW - Data analytics
KW - Multidisciplinary teaching
UR - http://www.scopus.com/inward/record.url?scp=85075422260&partnerID=8YFLogxK
U2 - 10.1145/3362789.3362841
DO - 10.1145/3362789.3362841
M3 - Conference contribution
AN - SCOPUS:85075422260
T3 - ACM International Conference Proceeding Series
SP - 534
EP - 538
BT - Proceedings - TEEM 2019
A2 - Conde-Gonzalez, Miguel Angel
A2 - Rodriguez-Sedano, Francisco Jesus
A2 - Fernandez-Llamas, Camino
A2 - Garcia-Penalvo, Francisco Jose
PB - Association for Computing Machinery
Y2 - 16 October 2019 through 18 October 2019
ER -