Developing High Performance Computing Resources for Teaching Cluster and Grid Computing courses

High-Performance Computing (HPC) and the ability to process large amounts of data are of paramount importance for UK business and economy as outlined by Rt Hon David Willetts MP at the HPC and Big Data conference in February 2014. However there is a shortage of skills and available training in HPC to prepare and expand the workforce for the HPC and Big Data research and development. Currently, HPC skills are acquired mainly by students and staff taking part in HPC-related research projects, MSc courses, and at the dedicated training centres such as Edinburgh University’s EPCC. There are few UK universities teaching the HPC, Clusters and Grid Computing courses at the undergraduate level. To address the issue of skills shortages in the HPC it is essential to provide teaching and training as part of both postgraduate and undergraduate courses. The design and development of such courses is challenging since the technologies and software in the fields of large scale distributed systems such as Cluster, Cloud and Grid computing are undergoing continuous change. The students completing the HPC courses should be proficient in these evolving technologies and equipped with practical and theoretical skills for future jobs in this fast developing area. In this paper we present our experience in developing the HPC, Cluster and Grid modules including a review of existing HPC courses offered at the UK universities. The topics covered in the modules are described, as well as the coursework projects based on practical laboratory work. We conclude with an evaluation based on our experience over the last ten years in developing and delivering the HPC modules on the undergraduate courses, with suggestions for future work

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Invited Paper: Teaching Information Systems in the Age of Digital Disruption

The Information Systems discipline has long suffered an identity crisis. It has also been prone to program sustainability issues as a technology focus has waxed and waned over the last 50 years. This paper suggests a new approach to teaching Information Systems, utilizing the notion of “fundamental and powerful concepts.” Using digital disruption as a fundamental and powerful concept, the authors argue for the core IS course and the courses that make up the major to be developed and centered around the transformation of business models, products, and services caused by emerging digital technologies. The paper includes an outline for the core IS course and the other courses in the major and concludes with a suggestion that the fundamental and powerful concept of digital disruption be used as an approach to teaching Information Systems

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Designing dissemination and validation of a framework for teaching cloud fundamentals

Three previous Working Groups (WGs) met at ITiCSE conferences to explore ways to help educators incorporate cloud computing into their courses and curricula by mapping industry job skills to knowledge areas (KAs). These WGs identified, organized, and grouped together student learning objectives (LOs) and developed these KAs and LOs in a repository of learning materials and course exemplars. This WG focused on the sustainability of the work of its predecessors through dissemination, community building and validation of the framework of KAs and LOs and its contribution to curriculum development. Firstly, a case study is presented which analyzed the implementation of a new Masters program which was based on the KAs and LOs. It was found that these provide a useful basis for program development and approval and demonstrate that successful program development of this nature can provide a valuable opportunity to communicate the work of the previous WGs. Thereafter, a plan was formulated for dissemination of the work done in order to drive adoption and to encourage instructors with an interest in teaching cloud computing to participate and grow the community. While the strategy included a range of dissemination methods, the importance of interaction with users was a guiding principle. Initial pilots of webinar and workshop activities have been implemented. Approaches to validating that a cloud computing course designed around the KAs and LOs can meet the needs of industry have been outlined with further iterations being considered. A research plan has been designed for a study to be implemented over the coming year in order to perform this validation

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

MSIS 2016 global competency model for graduate degree programs in information systems

[Extract] This document, “MSIS 2016: Global Competency Model for Graduate Degree Programs in Information Systems”, is the latest in the series of reports that provides guidance for degree programs in the Information Systems (IS) academic discipline. MSIS 2016 is the seventh collaborative effort between ACM and AIS (following IS’97, IS 2002, and IS 2010 at the undergraduate level; MSIS 2000 and MSIS 2006 at the graduate level; and CC 2005 as an integrative document).(undefined)info:eu-repo/semantics/publishedVersio