UW-Madison Resources for Data Science

This page is intended to help researchers and students navigate some of the on-campus resources available for learning and applying data science. It currently features campus communities & clubs, computing & data management resources, workshops, seminars, and the Researcher Toolkit. To suggest a new resource for this page, please see the Suggest a Resource section at the bottom of this page.

Researcher Toolkit

The Researcher Toolkit is for UW-Madison faculty, staff, and student researchers. It describes and points to on- and off-campus resources available for each phase of a research project, from planning to publishing your work.

Workshops

This is an accordion element with a series of buttons that open and close related content panels.

Carpentries Workshops

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.

Libraries Workshops

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.

Social Science Computing Cooperative (SSCC) Training Program

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.

Software Training for Students (STS) - DoIT

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.

Communities & Clubs

This is an accordion element with a series of buttons that open and close related content panels.

Computational Biology, Ecology, and Evolution (ComBEE)

ComBEE, at the University of Wisconsin-Madison, represents a dedicated group of researchers with a focus on computational biology, especially within ecology and evolution. This initiative enriches the community through weekly Python and R study sessions and monthly seminars that highlight the latest in computational biology. It is designed to support learners of all levels in bioinformatics and evolutionary biology, offering activities such as bioinformatics tool tutorials, discussions on evolutionary biology, and opportunities for collaborative research. ComBEE is a foundational platform for those seeking to enhance their knowledge and skills in these interdisciplinary fields.

Ethics, Values, Information, and Law (EVIL)

EVIL is a collaborative effort between the iSchool and ML+X that delves into the clash of Ethics, Values, Information technology, and Law. This group fosters scholarly discussion and research on the ethical implications, legal considerations, and societal impacts of data and information technologies. It serves as a unique forum for students, academics, and professionals to engage with pressing issues at the nexus of technology and humanity, promoting a deeper understanding and responsible use of technology in society.

Machine Learning Community (ML+X)

The ML+X community on campus brings together students, researchers, and industry professionals who share an interest in using machine learning (ML) methods (e.g., regression, classification, clustering, NLP, reinforcement learning, etc.) to advance their work (X). Community events and activities aim to help ML practitioners explore the challenges and pitfalls of ML, share knowledge and resources, and support each others’ work. Join the community to learn more!

Machine Learning for Medical Imaging (ML4MI)

The overall purpose of the Machine Learning for Medical Imaging (ML4MI) Initiative is to foster interdisciplinary collaboration between machine learning (ML) experts and medical imaging researchers at the University of Wisconsin, in order to develop and apply state-of-the-art ML solutions to challenging problems in medical imaging. This initiative responds to rapidly growing interest in ML techniques within medical imaging research, due to the unprecedented potential to solve challenging problems in areas such as image reconstruction, image processing, and computer-aided diagnosis. ML4MI is generously supported by the UW Departments of Radiology and Medical Physics, and the Grainger Institute for Engineering.

Student Organizations

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!

UW Carpentries (Workshop) Community

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.

Computing & Data Management

This is an accordion element with a series of buttons that open and close related content panels.

Center for High Throughput Computing

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.

NViDIA GPU Servers

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. Learn more about the Olvi GPU cluster

Research Cyberinfrastructure

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

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.

UW Data Storage Finder

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.

XSEDE

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.

Seminars

To review a full list of data science and machine learning related seminars offered on campus, please visit the Campus Seminars page.

Suggest a Resource

If you know of a good data science related resource, we would love to hear from you. Please suggest your resource by clicking the button below to fill out a short google from.

Suggest Resource