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).
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.
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 email@example.com 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).
The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce “Methods and Practice of Scientific Computing”, the first graduate course designed and organized by MICDE faculty. The course will be taught in Fall 2016, coordinated by Dr. Brendan Kochunas. This foundational course in scientific computing has been developed as a broad introduction to the subject, and has been designed to support research in all disciplines represented in MICDE. In addition to Brendan Kochunas, the course was developed by MICDE professors Bill Martin, Karthik Duraisamy, Vikram Gavini, and Shravan Veerapaneni, and MICDE Assistant Director Mariana Carrasco-Teja.
The details follow:
Prerequisites: Graduate standing and permission of instructor.
This course is designed for graduate students who are developing the methods, and using the tools, of scientific computing in their research. With the increased power and availability of computers to do massively scaled simulations, computational science and engineering as a whole has become an integral part of research that complements experiment and theory. This course will teach students the necessary skills to be effective computational scientists and how to produce work that adheres to the scientific method. A broad range of topics will be covered including: software engineering best practices, computer architectures, computational performance, common algorithms in engineering, solvers, software libraries for scientific computing, uncertainty quantification, verification and validation, and how to use all the various tools to accomplish these things. The class will have lecture twice a week and have an accompanying lab component. Students will be graded on homeworks, lab assignments, and a course project.
A Software Carpentry workshop will be held at the U-M Medical School May 2 and 3. These workshops are free and open to anyone on campus; the sessions are suitable for researchers in the humanities and social sciences. Register here.
This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.
Who: The course is aimed at graduate students, postdocs, and other researchers across the University of Michigan. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.
Where: Furstenberg 2710 (2nd floor of Med Sci II).