New geospatial analysis and GIS support at CSCAR

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CSCAR (Consulting for Statistics, Computing and Analytics Research) is offering expanded support for geospatial analysis and geographic information systems (GIS), effective immediately.

Researchers seeking guidance in this area are encouraged to schedule an appointment by calling 764-7828.

Several members of the CSCAR staff have expertise in modeling and analysis of geospatial data, and can provide consultations on basic and advanced methods. A variety of tools including R, Matlab, Python, and Arc-GIS are supported for work in this area. The CSCAR team was recently joined by a consultant holding a PhD in Earth/Environment Sciences, specializing in GIS and remote sensing.

As a result, CSCAR is now able to support a broad range of geospatial analysis activities including GIS, geostatistics, mechanistic modeling, geospatial visualization, and large-scale geospatial data processing on Flux and other advanced infrastructure systems. New workshops in Arc-GIS and other geospatial tools will begin in November (details will appear on this website).

U-M, Coursera offer five-course specialization in Applied Data Science with Python

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Coursera and the University of Michigan are offering a five-course specialization in Applied Data Science with Python starting in September. The courses cost $79 each, and students who complete all coursework, including a capstone project, will receive a Certificate.

The courses, taught by U-M faculty members Christopher Brooks (SI), Kevyn Collins-Thompson (SI and EECS), Daniel Romero (SI and EECS) and VG Vinod Vydiswaran (Medical School and SI), are:

  • Introduction to Data Science in Python
  • Applied Plotting, Charting and Data Representation in Python
  • Applied Machine Learning in Python
  • Applied Text Mining in Python
  • Applied Social Network Analysis in Python (Capstone project)

For more information, see the Coursera webpage.

Building a Community of Social Scientists with Big Data Skills: The ICOS Big Data Summer Camp

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As the use of data science techniques continues to grow across disciplines, a group of University of Michigan researchers are working to build a community of social scientists with skills in Big Data through a week-long summer camp for faculty and graduate students.

Having recently completed its fourth annual session, the Big Data Summer Camp held by the Interdisciplinary Committee for Organizational Studies (ICOS) trains approximately 50 people each spring in skills and methods such as Python, SQL, and social media APIs. The camp splits up into several groups to try to answer a research question using these newly acquired skills.

Working with researchers from other fields is a key component of the camp, and of creating a Big Data social science community, said co-coordinator Todd Schifeling, a Research Fellow at the Erb Institute in the School of Natural Resources and Environment.

“Students meet from across social science disciplines who wouldn’t meet otherwise,” said Schifeling. “And every year we bring back more and more past campers to present on what they’ve been doing.”

Schifeling himself participated in the camp as a student before taking on the role of coordinator this year.

Teddy DeWitt, the other co-coordinator of the camp and a doctoral student at the Ross School of Business, added the camp presents the curriculum in a unique way relative to the rest of campus.

“This set of material does not seem to be available in other parts of the university, at least … with an applied perspective in mind,” he said. “So we’re glad we have this set of resources that is both accessible and well-received by students.”

Participants range in skill from beginning to advanced, but even a relatively advanced student like Jeff Lockhart, a doctoral student in sociology and population studies who describes himself as “super-committed to computational social science,” said that it’s hard to find classes in computational methods in social science departments.

“[The ICOS camp] doesn’t expect a lot of prior knowledge, which I think is critical,” Lockhart said.

Lockhart, DeWitt, and Dylan Nelson, also a sociology doctoral student, are working on setting up a series of workshops in Computational Social Science for fall 2016 (contact Lockhart at jwlock@umich.edu for more information). Lockhart said it’s critical that social scientists learn Big Data skills.

“If we don’t have skills like this, there’s no way for us to enter into these fields of research that are going to be more and more important,” he said.

“A lot of the skills we’ve learned are sort of the on-ramp for doing data science,” DeWitt added.

The camp is co-sponsored by Advanced Research Computing (ARC).

Video, slides available: “Advanced Research Computing at Michigan, An Overview,” Brock Palen, ARC-TS

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Video (http://myumi.ch/aAG7x) and slides (http://myumi.ch/aV7kz) are now available from Advanced Research Computing – Technology Services (ARC-TS) Associate Director Brock Palen’s presentation “Advanced Research Computing at Michigan, An Overview.”

Palen gave the talk on June 27, 2016, outlining the resources and services available from ARC-TS as well as from off-campus resource providers.

MIDAS awards first round of challenge funding in transportation and learning analytics

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Four research projects — two each in transportation and learning analytics — have been awarded funding in the first round of the Michigan Institute for Data Science Challenge Initiatives program.

The projects will each receive $1.25 million dollars from MIDAS as part of the Data Science Initiative announced in fall 2015.

U-M Dearborn also will contribute $120,000 to each of the two transportation-related projects.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science.

New on-campus data-science and computational research services available

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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:

  • 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.

For more information, see the University Record article.

Krishna Garikipati appointed Director of MICDE

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Statement from S. Jack Hu, U-M Vice President for Research:

krishnaGarikipatiI’m very pleased to announce that Prof. Krishna Garikipati (Mechanical Engineering and Mathematics) has been appointed the new Director of the Michigan Institute for Computational Discovery and Engineering (MICDE). The Institute has grown significantly since its establishment in 2013 as the interdisciplinary home for the development and use of mathematical algorithms on high performance computers at U-M. Prof. Garikipati has been involved as associate director for research since Fall 2014 and is uniquely positioned to take the institute to the next level.

MICDE is a joint initiative of UMOR, the College of Engineering, and the College of Literature, Science and the Arts. In the past year, it has seen many new and important developments, including the launching of two centers focused on network and storage-enabled collaborative science and data-driven computational physics; new planned course offerings for the PhD in Scientific Computing and the Graduate Certificate in CDE; new initiatives on industrial engagement; and the establishment of the Scientific Computing Student Club. A number of new research initiatives are also being planned, with broadening participation of MICDE-affiliated faculty, whose numbers continue to grow.

Prof. Garikipati will take over the directorship of MICDE from Prof. Eric Michielssen (EECS) who founded the institute in Fall 2013 and served as director, in addition to his role as Associate Vice President for Advanced Research Computing. Prof. Michielssen will continue as AVP.