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UVA Data Points

UVA School of Data Science

1 Creator

a podcast exploring the world of data science

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Top 10 UVA Data Points Episodes

Best episodes ranked by Goodpods Users most listened

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02/01/23 • 70 min

This episode features a conversation between Lane Rasberry, Wikimedia-in-Residence at the University of Virginia School of Data Science, and Virginia Eubanks, author, journalist, and associate professor of political science at the University at Albany.

The conversation was recorded in 2019 but the topics are still relevant today. Eubanks looks toward the future, warning of the unintended—or at times intended—consequences of emerging technologies. The discussion focuses on the effects of algorithmic automation, as well as the practice, policies, and implementation of these algorithms. Although she critiques the tech world, Eubanks also provides many reasons for optimism.

Virginia Eubanks authored the 2018 book Automating Inequality, which is a detailed investigation into data-based discrimination. She is also the author of Digital Dead End: Fighting for Social Justice in the Information Age and the co-editor, with Alethia Jones, of Ain’t Gonna Let Nobody Turn Me Around: Forty Years of Movement Building with Barbara Smith. She also writes for various outlets, including the Guardian, American Scientist, and the New York Times. Recently, Virginia began the PTSD Bookclub, an ongoing project that explores books about trauma and its aftermath. You can find this project and Virginia Eubank’s other projects at virginia-eubanks.com.

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12/15/22 • 56 min

In this episode we’re bringing you a conversation on the future of academic data science recorded live at UVA Data Science’s Datapalooza 2022 event

Datapalooza is a flagship event for the School of Data Science. It’s typically held each year in November and features presentations by researchers here at UVA, as well as friends and collaborators of the School of Data Science.

In this episode we’re featuring a panel discussion between:

  • Doug Hague, the Executive Director at UNC-Charlotte’s School of Data Science
  • H.V. Jagadish, Director of the Michigan Institute for Data Science at the University of Michigan
  • Phil Bourne, Dean of the UVA School of Data Science
  • And Micaela Parker, Founder and Executive Director of the Academic Data Science Alliance. Micaela also serves as the moderator for this panel discussion.

Links:

Future of Academic Data Science video recording

Michigan Institue of Data Science

UNC Charlottes School of Data Science

UVA School of Data Science

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12/01/22 • 57 min

For our exploration of Analytics, we are diving into the world of sports. Because of advances in machine learning, wearable technology, and computer vision, the field of sport analytics is a whole new game. This episode gets into the details on what is new, the impact of analytics and technology on athletes and sports, as well as the ethics surrounding its implementation. Three experts from the University of Virginia School of Data Science met to discuss this exciting topic: Natalie Kupperman, Stephen Baek, and Don Brown.

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WikiProject Biography

UVA Data Points

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11/21/22 • 36 min

This bonus episode features a conversation between Lane Rasberry, Wikimedian-In-Residence at the UVA School of Data Science, and Lloyd Sy, a Ph.D. candidate in the UVA Department of English. In this conversation, Lane and Lloyd take a deep dive into the expansive world of Wikidata and ask the existential question, "What makes a person a person?" Or, more specifically, what data points make up a person? To help answer this question, Lloyd developed a large-scale data model of the biographical data contained within the Wikidata platform. This project serves as the foundation for their conversation. They also take a wide view of biographical data as it pertains to research and academia, including the process of gathering the data, the ethics of utilizing the data, personal ownership of the data, and much more. Anyone interested in these concepts should find this discussion valuable.

Links:

WikiProject Biography

Music:

"Screen Saver" Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/

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11/01/22 • 41 min

This episode on Systems explores the challenges of cloud computing within the framework of biomedical research. Phil Bourne, Dean of the UVA School of Data Science, speaks with computational biologist and associate professor Nathan Sheffield about a paper they co-wrote on systemic issues from cloud platforms that do not support FAIRness, including platform lock-in, poor integration across platforms, and duplicated efforts for users and developers. They suggest instead prioritizing microservices and access to modular data in smaller chunks or summarized form. Emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. The two discuss how funders, developers, and researchers can support microservices as the next generation of cloud-based bioinformatics.

From Cloud Computing to Microservices: Next Steps in FAIR Data and Analysis

https://pubmed.ncbi.nlm.nih.gov/36075919/

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10/26/22 • 37 min

The UVA School of Data Science was formed in September 2019 and has since grown in its collaborations, partnerships, program offerings, and teaching and research personnel. We are now constructing a new facility that will house the School of Data Science at the University of Virginia.

The new building is in the first phase of development and, once complete, will link the University's Central Grounds with the athletic fields and North Grounds. The 60,000-square-foot building is the future home of the UVA School of Data Science and will serve as the gateway to the new Emmet-Ivy Corridor and the Discovery Nexus.

This bonus episode is a conversation between UVA architect Alice Raucher and Mike Taylor, a principal with Hopkins Architects. Both Alice and Mike have been instrumental in the building’s design. Alice has also played a key role in the development of the Ivy Corridor. Mike and Alice take a deep dive into the thought process behind the building’s design, its relationship to the University and its history, the land's unique topography, and its significance to future projects along the Ivy Corridor.

Links:

Hopkins Architects

School of Data Science New Building Website

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Rafael Alvarado and Allison Bigelow discuss the Multepal project, which focuses on decolonization and the digital humanities through encoding the Popol Vuh, the Mayan book of creation, and other indigenous texts. Their research connects the humanities to data science by framing digitization and data design as interpretive acts that can have far reaching consequences for our understanding of history and society.

Multepal Links:

Digital Edition of the Popol Vuh: https://multepal.github.io/app-aanalte/xom-all-flat-mod-pnums-lbids.html

Multepal Project: https://multepal.spanitalport.virginia.edu/

Multepal GitHub: https://github.com/Multepal

Books Mentioned:

Mining Language by Allison Bigelow

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This bonus episode features Matthew Thomas, a data scientist at Inclusively and a graduate of the UVA M.S. in Data Science program. He talks about how Inclusively works to create and maintain a job board designed specifically for job seekers with disabilities. Matthew explains how typical job boards come with many built-in biases that can screen out qualified individuals without them even knowing. He discusses the challenges of removing biases from algorithms and the importance of honesty and self-criticism when examining a data science project.

As Cathy O’Neil challenged in Episode 1 of UVA Data Points, we should always ask ourselves, “For whom does this fail?” Matthew’s work is a good illustration of this sentiment in practice. In addition to discussing his work, Matthew also gives solid career advice for anyone seeking a similar career path in data science.

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09/01/22 • 27 min

UVA Data Points sits down with Cathy O'Neil, author of Weapons of Math Destruction, and Brian Wright, Assistant Professor of Data Science at the University of Virginia. The candid dialogue ranges from O'Neil's new book The Shame Machine to her work as an algorithm audit consultant. The two also draw comparisons between data science problems and knitting, as well as discuss educating future data scientists.

Links:

https://mathbabe.org (Cathy O'Neil's website)

https://datascience.virginia.edu (UVA School of Data Science website)

Books mentioned:

The Shame Machine

Weapons of Math Destruction

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08/24/22 • 8 min

Before diving into the complex world of data science it seemed to wise to establish a shared definition of the field. Here at the UVA School of Data Science, we have defined data science with the 4 + 1 Model. This model serves an outline for the first series of UVA Data Points. It also serves as a guiding definition within the School of Data Science, touching everything from research to course planning.

In this introduction trailer, host Monica Manney discusses the history, development, and function of the 4 + 1 Model of Data Science with its main author, Raf Alvarado.

Below is a brief expect from An Outline of the 4 + 1 Model of Data Science by Raf Alvarado:

“The point of the 4 + 1 model, abstract as it is, is to provide a practical template for strategically planning the various elements of a school of data science. To serve as an effective template, a model must be general. But generality if often purchased at the cost of intuitive understanding. The following caveats may help make sense of the model when considering its usefulness when applied to various concrete activities.

The model describes areas of academic expertise, not objective reality. It is a map of a division of labor writ large. Although each of the areas has clear connections to the others, the question to ask when deciding where an activity belongs is: who would be an expert at doing it? The realms help refine this question: the analytics area, for example, contains people who are good at working with abstract machinery. The four areas have the virtue of isolating intuitively correct communities of expertise. For example, people who are great at data product design may not know the esoteric depths of machine learning, and that adepts at machine learning are not usually experts in understanding human society and normative culture.

Each area in the model contains a collection of subfields that need to be teased out. Some areas will have more subfields than others. Although some areas may be smaller than others in terms of number of experts (faculty) and courses, each area has a major impact on the overall practice of data science and the quality of the school’s activities. In addition, these subfields are in an important sense “more real” than the categories. We can imagine them forming a dense network in which the areas define communities with centroids, and which are more interconnected than the clean-cut image of the model implies.

The areas of the model are like the components of a principal component analysis of the vector space of data science. They capture the variance that exists within the field, and, crucially, provide a framework for realigning (rebasing) the academy along a new set of axes. One effect of this is to both disperse and recombine older fields, such as computer science, statistics, and operations research, into new clusters. Thus we separate computer science subfields such as complexity analysis and database design. One possible salutary result of this will be the formation of new syntheses of fields that share concerns but differ in vocabularies and customs..."

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