Finding the Needles in the Haystack: Identifying Essential Concepts and Tools for Inclusion in a Cross-Listed Introductory Semester Course in Cloud Computing

• Main Authors: Striki, Maria, Haghani, Sasan
• Format: Conference Proceeding
• Language: English
• Subjects: Cloud computing; Computational modeling; Data science; Industries; introductory course; Machine learning; Needles; Parallel processing; teaching strategies
• ISSN: 2165-9567
• DOI: 10.1109/EDUCON60312.2024.10578822

Cloud computing is among the most rapidly advancing domains and computing paradigms due to its significant impact on the advancement of technology, scientific research, the business market, and its influence on social life. Cloud services have become the computational model for many related disciplines, such as data science and machine learning. Moreover, the adoption of cloud computing services by industries and individuals is generating increased demand for industry skills in this domain. To address this expansion, higher education institutions have recognized the importance of incorporating cloud computing courses into their engineering curricula. This paper presents the development of a new Cloud Computing and Big Data multidisciplinary course at the Department of Electrical and Computer Engineering at Rutgers University in order to successfully train students in the competitive area of cloud computing. This paper will elaborate on, and provide rationale for the selection of course content, topics, tools, and the chosen pedagogical approach from a vast array of instructional materials, all aimed at delivering this course. Our aim has been to provide students with a strong foundation and self-initiative in this area so that even after the end of the course, they are fully capable of educating themselves and progressing in the demanding and competitive area of cloud computing. The course is unique in its interdisciplinary goals combining fields together such as: distributed and parallel computing, mathematics, programming and programming models, systems design, data science, and machine learning. Samples of students' projects and presentations that have resulted from the course offering will be presented. Moreover, we will further analyze and elaborate the "year by year" fine-tuning and adaptation of the material, tools, and teaching strategies used, based on our learning experiences as instructors, on our successful and unsuccessful strategies, and on the feedback we have received from students, from colleagues, and from industry employers.