CSCAR announces a reading and discussion group Data science in understanding and addressing climate change that will meet on the third or fourth (depending on the preferences of participants) Friday of every month between 3 and 5 pm. We will discuss reports and significant papers that illuminate fundamental issues in climate change science, policy, and management. The suggested format at this stage is that we discuss one science and one policy (or management) paper or chapter. The focus will be on the spatial (and temporal) dimensions of the issue and we will concentrate more on methods and techniques keeping the requirement for domain knowledge relatively low. We will lay emphasis on the conceptual part of the tools and techniques so that it is accessible to a wider set of participants, but will also get into the technical details.
This is an effort to bring people involved in climate change together from a data science perspective. The idea is to learn together in a fun environment and foster dialogue with a focus on how data science can provide the common ground for mutual learning and understanding.
We will meet in Rackham, but we will be open to rotating the location. You will be able to participate remotely, if you choose to.
If you are interested send an email to Manish Verma at email@example.com
If you have any suggestion for discussion and reading let us know. We will include chapters from the IPCC and US global change science programs in our discussion.
In this competition, student teams at the University of Michigan will use historical free agent data to predict the value of new contracts signed in the 2018 free agency period. These predictions will be evaluated against the actual contracts as they are signed. This competition is organized by the Michigan Data Science Team (MDST), in collaboration with the Baltimore Ravens and the Michigan Sports Analytics Society (MSAS). Food will be provided. This is an initial kick-off meeting of the competition.
500 Church St, 48104, MI
CSCAR now provides walk-in support during business hours for students, faculty, and staff seeking assistance in getting started with the Flux computing environment. CSCAR consultants can walk a researcher through the steps of applying for a Flux account, installing and configuring a terminal client, connecting to Flux, basic SSH and Unix command line, and obtaining or accessing allocations.
In addition to walk-in support, CSCAR has several staff consultants with expertise in advanced and high performance computing who can work with clients on a variety of topics such as installing, optimizing, and profiling code.
Support via email is also provided via firstname.lastname@example.org.
CSCAR is located in room 3550 of the Rackham Building (915 E. Washington St.). Walk-in hours are from 9 a.m. – 5 p.m., Monday through Friday, except for noon – 1 p.m. on Tuesdays.
See the CSCAR web site (cscar.research.umich.edu) for more information.
Advanced Research Computing at U-M (ARC) will host an information session for graduate students in all disciplines who are interested in new computing and data science resources and services available to U-M researchers.
Brief presentations from members of ARC Technology Services (ARC-TS) on computing infrastructure, and from Consulting for Statistics, Computing, and Analytics Research (CSCAR) on statistics, data science, and computing training and consulting will be followed by a Q&A session, and opportunities to interact individually with ARC and CSCAR staff.
ARC and CSCAR are interested in connecting with graduate students whose research would benefit from customized or innovative computational or analytic approaches, and can provide guidance for students aiming to do this. ARC and CSCAR are also interested in developing training and documentation materials for a diverse range of application areas, and would welcome input from student researchers on opportunities to tailor our training offerings to new areas.
- Kerby Shedden, Director, CSCAR
- Brock Palen, Director, ARC-TS
Wednesday, Nov. 8, 2017, 2 – 4 p.m., West Conference Room, 4th Floor, Rackham Building (915 E. Washington St.)
More than 50 students took part in the Student Poster Competition during the 3rd Annual MIDAS Symposium.
The winning entries can be viewed at http://midas.umich.edu/2017-symposium/winning-posters/
The University of Michigan Library system and the Data Acquisition for Data Sciences program (DADS) of the U-M Data Science Initiative (DSI) have recently joined forces to license a major data resource capturing parcel-level information about the property market in the United States.
The data were licensed from the Corelogic corporation, who have assimilated deed, tax and foreclosure information on nearly all properties in the entire US. Coverage dates vary by county, some county records go back fifty years. Coverage is more comprehensive from the 1990s to the present.
These data will support a variety of research efforts into regional economies, economic disparities, trends in land-use, housing market dynamics, and urban ecology, among many other areas.
The data are available on the Turbo Research Storage system for users of the U-M High Performance Computing infrastructure, and via the University of Michigan Library.
To access the data, researchers must first sign a MOU; contact Senior Associate Librarian Catherine Morse email@example.com for more information, or visit https://www.lib.umich.edu/database/corelogic-parcel-level-real-estate-data.
Asst. Prof. Emanuel Gull, Physics, is offering a mini-course introducing the Python programming language in a four-lecture series. Beginners without any programming experience as well as programmers who usually use other languages (C, C++, Fortran, Java, …) are encouraged to come; no prior knowledge of programming languages is required!
For the first two lectures we will mostly follow the book Learning Python. This book is available at our library. An earlier edition (with small differences, equivalent for all practical purposes) is available as an e-book. The second week will introduce some useful python libraries: numpy, scipy, matplotlib.
At the end of the first two weeks you will know enough about Python to use it for your grad class homework and your research.
Special meeting place: we will meet in 340 West Hall on Monday September 11 at 5 PM.
Please bring a laptop computer along to follow the exercises!
Syllabus (Dates & Location for Fall 2017)
- Monday September 11 5:00 – 6:30 PM: Welcome & Getting Started (hello.py). Location: 340 West Hall
- Tuesday September 12 5:00 – 6:30 PM: Numbers, Strings, Lists, Dictionaries, Tuples, Functions, Modules, Control flow. Location: 335 West Hall
- Wednesday September 13 5:00 – 6:30 PM: Useful Python libraries (part I): numpy, scipy, matplotlib. Location: 335 West Hall
- Thursday September 14 5:00 – 6:30 PM: Useful Python libraries (part 2): 3d plotting in matplotlib and exercises. Location: 335 West Hall
Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided
- The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
- The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
- The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
1) Modeling — Understanding of core data science principles, assumptions and applications;
2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
3) Practice — Hands-on experience with real data, modeling tools, and technology resources.
Times / Locations:
Want to effectively use untapped data that is all around us—from traffic streaming data to social media data—to discover a desired solution? A new University of Michigan-Dearborn graduate program is going to teach students to do just that.