The “Dive Into Big Data” high school-level workshop is a great way to introduce students to the fundamentals of Data Science, as they complete an interactive experiment with live oceanographic data (courtesy of the Ocean Observatory Institute, or OOI) and visualize the results of simple data transformations. The materials for this workshop have been made available so that educators can host their own Dive Into Big Data workshop.
These materials include a description of the program, a workshop agenda for those planning on touring the Advanced Cyberinfrastructure facilities, a presentation giving an overview of the objectives of the program, and hands-on step-by-step instructions for how to complete the workshop using live data from the OOI.
After the activity is completed, students will take a short quiz and survey to assess the effectiveness of the workshop’s objectives. Questions regarding the RDI² facilities tour may be omitted or modified if the workshop was hosted from an external location.
RDI²’s VDC project team has hosted several events for undergraduates, graduate students, and beyond, including distinguished seminar series and roundtables.
Going forward, these events will be posted on our YouTube channel for instructors to utilize; one such event is the Data Science Career Panel, featuring industry speakers from within the field of Data Science. The speakers answer questions from prospective computer and data science students regarding what they can expect once entering the field professionally. This roundtable event is a useful resource for undergraduate and high school students interested in pursuing a career in data science.
As part of the NSF funded Virtual Data Collaboratory project, RDI2 is developing educational modules to help researchers solve their data issues and increase the impact of their research. One such module was the Introduction to Data Management seminar; held during May 2018, this seminar invited career researchers to a join RDI2 for a discussion of best practices for managing research data. The data created as part of research is important, and should be well-organized, well-preserved, accessible, understandable, and usable by the scholarly community.
The discussion included developments in data sharing, data collaboration, reproducible research, and more. Insights and feedback shared during the seminars are further incorporated into the educational modules, the materials for which have been made available as resources for educators.
Scientific Computing is a useful tool for scientists to achieve better research results, faster. This talk provides an introduction to Scientific Computing as it relates to High-Performance Computing.
Philip Davis, Application Developer
Rutgers Discovery Informatics Institute
Rutgers, The State University of New Jersey
Learn how applications running at extreme scales in supercomputers suffer from data related problems and how Rutgers is tackling extreme scale data management challenges.
Pradeep Subedi, PhD, Research Associate
Rutgers Discovery Informatics Institute
Rutgers, The State University of New Jersey
Developing applications for High-Performance Computing requires specialized knowledge and technologies. This talk provides an overview of these technologies, with a focus on their application to the domain of Scientific Computing.
Philip Davis, Application Developer
Rutgers Discovery Informatics Institute
Rutgers, The State University of New Jersey
The volume of new data being generated is overwhelming the capacity of institutions and researchers to make use of it. Processing this data requires managing heterogeneous, distributed and mobile resources in near real-time. This lecture introduces principles to support advanced analytics and sensors applications.
Daniel Balouek-Thomert, PhD, Research Associate
Rutgers Discovery Informatics Institute
Rutgers, The State University of New Jersey
Brief introduction to Data Management and the Virtual Data Collaboratory.
Ryan Womack, Data Librarian
Rutgers, The State University of New Jersey
Designing experiments, acquiring data and producing results such as plots and articles takes a number that, if incorrectly executed, might jeopardize an entire project or silently produce wrong results. This video introduces concepts such as experimental workflow and data life cycle, then proceeds to provide viewers with advice and good practices for optimizing the life cycle of their data and of their own experimental workflows to make their work more efficient and safe.
Anthony Simonet, PhD, Research Associate
Rutgers Discovery Informatics Institute
Rutgers, The State University of New Jersey
This lecture introduces the fundamentals of data science.
Anthony Simonet, PhD, Research Associate
Rutgers Discovery Informatics Institute
Rutgers, The State University of New Jersey