UW-Madison Resources for Data Science

In collaboration with the Carpentries Community, the Data Science Hub offers a variety of data science and machine learning workshops throughout the calendar year that teach programming and data analysis (Unix shell, Python, R, OpenRefine), version control (Git/GitHub), database management (SQL), containerization (Docker), and machine learning (Intro to ML, Deep learning). Some of the subject area focuses for these workshops are geospatial, health sciences, ecology, social sciences, genomics, and the humanities. The Data Science Hub can also develop trainings customized for specific classes and labs.

• Upcoming workshops: Upcoming workshops are announced every two weeks in the Data Science Hub newsletter — subscribe to receive the newsletter in your inbox.
• Past workshops: To view all past workshops, please visit the Hub’s Training Resources page.
• Request a workshop: The Data Science Hub helps to organize custom, small-group workshops by local Carpentries instructors. If interested, please fill out the “Request a workshop” form.

The Libraries at UW-Madison regularly offers workshops that teach programming skills in both R and Python.

• Upcoming workshops: Upcoming workshops are announced via the Libraries Google group. Note that you need to be signed into google via your wisc account in order to see a “Join group” button on the Libraries Google group landing page.

The primary purpose of the SSCC’s training program is to give social science graduate students with an interest in quantitative research the skills they need to do research with real-world data. It is intended to complement formal coursework in statistical analysis, so there is a heavy emphasis on the data wrangling skills that usually are not taught in such classes. The curriculum was developed by the SSCC’s statistical consultants and draws on their long experience assisting social science graduate students as they begin their research.

While the primary audience for SSCC training is social science graduate students, the skills taught will be valuable to a much broader audience: graduate students in other fields, faculty and staff researchers interested in enhancing their skills or learning a new statistical package, or undergraduates who are interested in graduate school, data-driven careers, or just gaining a deeper understanding of statistical software so they can excel in classes that use it. SSCC’s training is free to all UW-Madison faculty, staff, and students.

• Upcoming workshops: View the in-person schedule and register for SSCC classes, or visit the online curriculum.

DoIT’s trainers can help students and instructors achieve success by teaching the technology skills needed in the classroom and beyond. They offer free technology training and project support to registered UW-Madison students and instructors. Visit the STS website to learn more.

There are many student run organizations on campus which cater to those interested in software development, data science, artificial intelligence, and more. Visit the CDIS student org website to learn more!

The ML Community on campus brings together machine learning (ML) practitioners across a variety of departments and disciplines, and provides a space for both novice and experienced ML practitioners to advance their ML skill sets, share knowledge and resources, and form collaborations. The community gathers monthly at an ML+X forum to discuss how others have been applying ML tools to their projects. They also help host ML workshops including a introductory ML workshop in Python and a deep learning workshop in Keras.

The University of Wisconsin-Madison has a long-standing relationship with the Carpentries, a global organization of researchers who volunteer their time and effort to create workshops that teach software engineering and data analysis skills to other researchers. The local community of workshop instructors, helpers, and lesson developers includes research support staff from across campus, faculty members, and graduate students. If you’re interested in learning more about getting involved with the community of instructors and helpers at UW-Madison, you can visit the UW Carpentries Community page. There are always opportunities to help or teach at a workshop, contribute to new workshop lesson development, or become a certified Carpentries instructor.

The Center for High Throughput Computing (CHTC) manages over 20,000 cores and dozens of GPUs for the UW-Madison campus; this resource, which is free and shared, aims to advance the mission of the University of Wisconsin in support of the Wisconsin Idea. Researchers can place their workloads on an access point at CHTC and utilize the resources at CHTC, across the campus, and across the nation.

The two fully equipped 8xA100 NViDIA GPU servers, named Olvi-1 and Olvi-2, are a dedicated campus resource for data science research. The servers are connected to the UW high-speed network, with access to other UW computation and data storage resources. While there are many GPUs in use on campus, these top-of-the-line units will allow researchers to address computing bottlenecks by running larger jobs.

The UW-Madison Computer Systems Lab is administering the GPU servers. DSI researchers, including those working on projects funded through the American Family Funding Initiative, have priority for GPU server use. Computational cycles not used by DSI researchers and affiliates will be made available to campus researchers in an inclusive and equitable manner.

For more information, contact Steve Goldstein, DSI Program Manager: sgoldstein@wisc.edu.

Research Cyberinfrastructure (RCI) is a university-wide effort to expand and update UW–‍Madison’s research Cyberinfrastructure resources. The DoIT RCI team hosts a suite of technology and supports researchers using that technology – everything from a high-performance network, to research data storage, to technical support.

Research Data Services (RDS) is an interdisciplinary organization committed to advancing research data management practice on the UW-Madison campus. We focus on providing researchers with the tools and resources that support their efforts to store, analyze and share data. RDS services are free to UW-Madison faculty, researchers, staff, and graduate students.

The UW Data Storage Finder focuses on centralized campus research storage services for researchers. Many of the storage services are also available for teaching, outreach, or administrative use cases.

The Extreme Science and Engineering Discovery Environment (XSEDE) is a single virtual system that scientists can use to interactively share computing resources, data and expertise. If you are a US-based researcher and currently use, or want to use, advanced research computing resources and services, XSEDE can help.