Data Science Updates is the University of Wisconsin-Madison's resource for news, training, events, and professional opportunities in data science, brought to you by the Data Science Institute, powered by American Family Insurance, and the Data Science Hub.
September 3, 2025
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Planning for the 2026 Data Science Research Bazaar is underway, and we’d love your help! The Research Bazaar is an annual conference that fosters community around computing and data science across campus and the Madison community. This year’s event will take place on March 19, 2026, followed by extended tutorials and interactive discussions in the weeks that follow.
To ensure broad representation, the planning committee aims to include voices from across campus, Madison, and various domains of computing and data science. Data science expertise is not required, and we encourage people with experience or interest in learning about event planning, fundraising, and communications to participate. Planning meetings will be held approximately every two weeks. If you're interested in joining, please complete the volunteer form by September 21. The first planning meeting will be held the week of September 29.
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The Data Science @ UW website is your connection to data science research, resources, and learning and networking opportunities on our campus, as well as the many institutes, centers, programs, departments, and people working in data science here. Data Science @ UW is a collaborative effort that includes the Data Science Hub, Data Science Institute, School of Computer, Data & Information Sciences, and other data science-focused organizations at UW–Madison.
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Thank You, Olivia!
For the past year, law student Olivia Messerges has worked tirelessly to bring this newsletter to your inbox. She has also supported Data Science Hub workshops and related communities of practice. Olivia is moving on to new opportunities. We thank her for her many contributions to data science at UW and wish her all the best!
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September 8, 5:30 p.m. - 7:00 p.m.; 2257 College Library & Zoom. Curious about coding but don’t know where to start? This workshop unpacks programming and coding from the ground up. It explains the terms and structures used in most modern programming languages and introduces some basic computer science concepts in an accessible, easy to digest format with no technical background knowledge required.
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September 8, 5:30 p.m. - 7:00 p.m.; 2538 Morgridge Hall & Zoom AND September 9, 5:30 p.m. - 7:00 p.m.; 2257 College Library & Zoom AND September 22, 5:30 p.m. - 7:00 p.m.; 2257 College Library & Zoom. Python is a powerful, popular programming language that gets used for just about everything: basic coding, graphics processing, machine learning, AI, web scraping, data analysis, and much more. This workshop introduces basic Python concepts that will help you get coding in no time – no previous programming experience required!
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September 9, 12:00 p.m. - 2:00 p.m.; Zoom. NVivo is a popular qualitative data analysis software application that can be used to analyze interview transcripts and textual survey responses. NVivo facilitates data storage and organization and provides text coding tools. NVivo also provides analytical tools, such as word frequency analysis and matrix-coding analysis.
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September 9, 5:30 p.m. - 7:00 p.m.; 2538 Morgridge Hall & Zoom. This beginner-friendly workshop introduces you to Visual Studio Code (VS Code) — a free, open-source, and highly customisable code editor. Whether you’re new to coding or transitioning from another editor, this session is designed to help you confidently set up and navigate VS Code.
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September 10, 5:30 p.m. - 7:00 p.m.; 2257 College Library & Zoom. In this workshop, students explore the favorite tool of many creative professionals, Adobe Illustrator. Used by designers and artists around the world, this powerful vector graphics program makes it easy to create logos, posters, product labels, icons, website mockups, and more. Students will learn the basics of Illustrator, work with shapes, text, and color, and create a project from scratch.
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September 15, 5:30 p.m. - 7:00 p.m.; 2538 Morgridge Hall & Zoom. This workshop was curated to facilitate an understanding of the broad topic of artificial intelligence for both beginners and those with prior programming experience. Our engaging session will demystify complex concepts like gradient descent, learning rates, privacy, and bias. We will also delve into supervised and unsupervised learning, empowering you with foundational AI knowledge that transcends programming proficiency.
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September 16, 10:00 a.m. - 12:00 p.m.; Zoom AND September 23, 10:00 a.m. - 12:00 p.m.; Zoom. This workshop is for the absolute beginner wanting to slowly walk through the process of getting started with Python, a programming language commonly used for data analysis. We’ll work through the installation and setup of some helpful software and introduce basic concepts and terminology used in Python. Finally, we’ll create your first simple but useful program!
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September 16, 12:00 p.m. - 1:00 p.m.; Zoom. Are you interested in learning about using data from the ICPSR repository (the Inter-university Consortium for Political and Social Research database) and other social science data sources? Join us for a demo of how to use ICPSR and an open discussion on accessing other social science data.
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September 16, 5:30 p.m. - 7:00 p.m.; 2257 College Library & Zoom. As one of the flagship programs of the modern publishing industry, Adobe InDesign is used for all kinds of print and digital publications. In this workshop, students will learn how to set up projects in the InDesign workspace, incorporate external text and images, add styling to type and graphics, and export projects for publishing.
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September 18, 1:00 p.m. - 3:00 p.m.; 4218 Sewell Social Sciences. MaxQDA is a popular qualitative data analysis software that allows for organization, storage, coding, and analysis of qualitative data, including text, images, and video. This course will introduce the MaxQDA interface and cover the following topics: importing and cleaning data, organizing your project, coding data, and tools for performing data analysis.
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September 19, 10:00 a.m. - 12:00 p.m.; Zoom. This workshop is for beginners who want to slowly walk through the process of starting with R, a programming language commonly used for data analysis. The session will introduce you to the RStudio interface for coding in R. We will work through setting up a project directory, covering key concepts and terminology, and loading and inspecting a dataset. This workshop is for programming novices, so no previous experience is required.
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September 22, 5:30 p.m. - 7:00 p.m.; 2538 Morgridge Hall & Zoom. This course teaches object-oriented programming (OOP) and its corresponding syntax in Python. By the end of this course, the learner will be able to use OOP to more efficiently accomplish programming tasks, install external Python modules, and create their own Python modules. Learners should have have a basic understanding of procedural programming in Python.
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September 23, 1:00 p.m. - 3:00 p.m.; 4218 Sewell Social Sciences. NVivo is a popular qualitative data analysis software that allows for organization, storage, coding, and analysis of qualitative data, including text, images, and video. This course will introduce the NVivo interface and cover the following topics: importing data to NVivo, organizing and coding data, and performing analysis.
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Have questions about anything data science-related? Come see the Data Science Hub facilitators at Coding Meetup on Tuesdays and Thursdays from 2:30-4:30 p.m. CT. To join Coding Meetup, join data-science-hubgroup.slack.com.
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September 4, 1:00 p.m. - 2:00 p.m.; 7560 Morgridge Hall. Statistical models for spatially-dependent, discrete-valued observations associated with areal units are used routinely in disease mapping and image analysis. Despite a rich literature on classical and Bayesian models for this setting, theoretical and computational considerations remain. To address existing models' computational and theoretical concerns, Dr. Kate Calder and her team from the University of Texas-Austin propose a novel modeling framework based on a mixture of directed graphical models.
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September 4, 1:30 p.m. - 2:30 p.m.; UW Biotechnology Center Auditorium, Genetics-Biotechnology Center Building & Zoom. Hear from Dr. Brian Davis, University of Texas A&M. Dr. Davis is an evolutionary biologist who is interested in how genomes and organisms evolve when under natural and artificial selection.
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September 9, 11:00 a.m. - 12:00 p.m.; Zoom. Dr. Ehsan Adeli, Stanford University, will discuss how recent computer vision and computational neuroscience advances contribute to discovering behavioral and neural phenotypes of healthy aging and neurological disorders. Dr. Adeli will describe how such data-driven approaches could be used to discover movement-linked heterogeneity in neurodegenerative diseases.
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September 9, 1:00 p.m. - 2:00 p.m.; Zoom. Join the ML+X community and Nidhal Jegham, lead author of How Hungry is AI?, who will introduce a new benchmark that measures the energy, water, and carbon costs of LLM inference. Jegham's work develops clever methods to estimate consumption across open-source and proprietary models despite limited transparency, and provides a framework that weighs accuracy against resource use—helping practitioners, researchers, and everyday users select models more mindfully.
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Register individually by September 10 and with teams by September 18. The 2025 Machine Learning Marathon (MLM25) is a 12-week applied ML/AI hackathon from September 11 to December 11 at UW–Madison. Participants form teams of 2-5 and work on real-world challenges like sustainable AI, protein modeling, brain decoding, biodiversity image clustering, retail forecasting, and more. The event includes workshops, advisor support, AWS credits, and cloud training. Open to anyone with ML, Python, and Git+GitHub fundamentals (enough to collaborate on a team).
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September 10, 9:00 a.m. - 11:00 a.m.; 1145 Discovery Building. The monthly ML+Coffee event offers a casual and supportive environment to discuss ongoing projects, share tools, and network with other ML/AI practitioners. All backgrounds and experience levels are welcome. Most of our attendees are applied practitioners wrestling with their projects, not ML/AI purists looking to critique. Coffee is provided! ML+Coffee will also meet on 10/8, 11/5, and 12/17.
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September 17, 12:00 p.m. - 1:30 p.m.; Zoom. As a new academic year begins, what are the current implications of generative AI for teaching, learning, and the professional skills students need to develop? Hear from Emily Laird, M.S., a nationally recognized expert on AI integration in higher education who has worked closely with University of Wisconsin leaders to shape system-wide AI initiatives.
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September 19, 4:00 p.m.; Henry Vilas Park. Come one, come all! Inviting all students, staff, faculty, and friends to the annual CS Fall Picnic!
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September 30, 12:00 p.m. - 1:30 p.m.; Zoom. Gain the foundational knowledge to understand and adapt to how generative AI is changing teaching and learning. We’ll explain core concepts, discuss opportunities and risks, and explore UW–Madison enterprise AI tools, including the newest addition, NotebookLM. Hear from specialists in DoIT Academic Technology, Libraries, Writing Across the Curriculum, and CTLM.
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- AI & Data Science, UW-Madison Department of Plant and Agroecosystem Sciences
- Animal Research Technician, Morgridge Institute for Research
- Computer Support Technician, UW-Madison College of Letters & Science, Helen C. White IT
- Finance Student Assistant, UW-Madison Department of Family Medicine and Community Health
- Junior Web Editor, Wisconsin Historical Society
- Lab Assistant in Bacterial Genetics, UW-Madison School of Pharmacy, Pharmaceutical Sciences Division
- Web Developer Student Employee, UW-Madison College of Engineering, Industrial Refrigeration Consortium
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DATA VISUALIZATION OF THE WEEK
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This chart compares emissions in kilograms of CO2eq produced per kilogram of food product. The red bars show greenhouse emissions we would have if we removed methane completely; the grey bar shows the emissions from methane. The red and grey bar combined is therefore the total emissions, including methane.
How we treat the climate impacts of methane matters a lot for the carbon footprint of foods. But even if we exclude methane, meat and dairy products emit the most.
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Reposted from Our World in Data: Research and data to make progress against the world’s largest problems.
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Data Science Updates is a collaborative effort of the Data Science Institute and Data Science Hub. This newsletter was originally created by the Data Science Hub and published as Hub Updates.
Use our submission form to send us your news, events, opportunities and data visualizations for future issues.
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