Researchers across campus now have access to several new services to help them navigate the new tools and methodologies emerging for data-intensive and computational research.
As part of the U-M Data Science Initiative announced in fall 2015, Consulting for Statistics, Computing and Analytics Research (CSCAR) is offering new and expanded services, including guidance on:
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• Research methodology for data science.
• Large scale data processing using high performance computing systems.
• Optimization of code and use of Flux and other advanced computing systems.
• Advanced data management.
• Geospatial data analyses.
• Exploratory analysis and data visualization.
• Obtaining licensed data from commercial sources.
• Scraping, aggregating and integrating data from public sources.
• Analysis of restricted data.
“With Big Data and computational simulations playing an ever-larger role in research in a variety of fields, it’s increasingly important to provide researchers with a comprehensive ecosystem of support and services that address those methodologies,” said CSCAR Director Kerby Shedden.
As part of this significant expansion of its scope, the campuswide statistical consulting service CSCAR has been renamed Consulting for Statistics, Computing and Analytics Research. It was formerly known as the Center for Statistical Consultation and Research.
The updated name reflects new aspects of CSCAR’s mission: to support researchers across the U-M campus engaged in data-intensive and computational research, building on the focused consulting in statistical analyses that it has provided since the early 1970s.
Along with the services listed above, CSCAR is hosting a Data Science Skills Series focused on Python, R and other computing tools, covering topics such as data management, graphics and visualization, statistical analysis, geospatial analysis and more.
For information on accessing any of these services, visit CSCAR’s Consulting page. Many of CSCAR’s services are available at no charge to U-M researchers.
CSCAR will continue to expand its workshops and consulting services to meet the evolving needs of the data and computational science research community at U-M.
