TY - GEN
T1 - Curriculum Analysis for Data Systems Education
AU - Miedema, Daphne
AU - Alawini, Abdussalam
AU - Taipalus, Toni
AU - Goodfellow, Martin
AU - Ajanovski, Vangel V.
AU - Liut, Michael
AU - Peltsverger, Svetlana
AU - Young, Tiffany
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s)
PY - 2024/7/8
Y1 - 2024/7/8
N2 - The field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices.
AB - The field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices.
KW - curriculum
KW - data systems
KW - database
KW - education
KW - industry
KW - knowledge gap
KW - skill set
KW - student
UR - http://www.scopus.com/inward/record.url?scp=85198647280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85198647280&partnerID=8YFLogxK
U2 - 10.1145/3649405.3659529
DO - 10.1145/3649405.3659529
M3 - Conference contribution
AN - SCOPUS:85198647280
T3 - Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE
SP - 761
EP - 762
BT - ITiCSE 2024 - Proceedings of the 2024 Conference Innovation and Technology in Computer Science Education
PB - Association for Computing Machinery
T2 - 29th Conference Innovation and Technology in Computer Science Education, ITiCSE 2024
Y2 - 8 July 2024 through 10 July 2024
ER -