This resource contains teaching materials for an overview course in research computing.
The target audience is primarily college students, particularly graduate students, who conduct academic or scientific research. The information will also be useful for working professionals.
The main goal of this course is to help students improve their technical readiness for engaging in the increasingly data-centric work that they face in their degree programs, in their related research, and in their future professional and research practice. Students will become acquainted with key concepts in research computing and data management, including overviews of systems analysis techniques, data security and integrity, and database tools, among others.
At the end of this course students should be able to:
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Analyze requirements for management of data in different situations and projects.
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Choose appropriate technical tools and techniques to support that data management.
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Identify hazards and pitfalls in data-related projects.
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Identify factors that affect performance in the collection and preparation of data for analysis.
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Describe the core technologies most frequently employed in large-scale research data management.
The files hosted in the repository consist of the presentation slides in Markdown format as well as transcripts to go with those slides. The transcripts are posted as wiki pages in ASCIIDoc format and are also offered as PDF and EPUB eBooks.
Thanks to the following people who have contributed to this resource (in no particular order):
Brian High, Jim Hogan, John Yocum, Elliot Norwood, Lianne Sheppard, Boris Reiss, Noah Simon, and Ali Shojaie
Copyright © The Research Computing Team. This information is provided for educational purposes only. See LICENSE for more information. Creative Commons Attribution 4.0 International Public License.