Archive

U of M Day of Data 2024

Applications of Data Science

• Tanushree Naik - GEMS
  Multi-trait Prediction Algorithm and the Underlying Bayesian Stats Package

• Ryan Noe, Uspatial/MCAP, Twin Cities
  Creating Climate Change Data Products: From Model Outputs to Analysis on the Web

• Judee Sharon and Kate Adamala, College of Biological Science, Twin Cities
  Biocomputing taken to its logical conclusion: TRUMPET

• Caitlin Mills, Educational Psychology, Twin Cities
  Machine learning approaches to predict student engagement

Data Stories 

• Lacey Loomer, Department of Economics and Health Care Management, UMD
  Quality of long-term care facilities

• Ron Huesman & Helen Horner, Institutional Data and Research (IDR), Twin Cities
  Modeling Postcollegiate Outcomes of Students From Rural/Urban Backgrounds: Exploring Wage Parity in Minnesota

• Valeria Lopez Torres, College of Design, Twin Cities
  Where is the human in the data? Exploring AI through art, creativity and storytelling

• Alexis Elder, Philosophy Department (CAHSS), UMD
  Ethics of Digital Remains

U of M Day of Data 2023

Community engagement & advancing equity with data

• Ed Brands, Environmental Studies (Morris)
  Food Assessment in a Rural Context - Stevens County, MN

• David Haynes, Institute for Health Informatics (Twin Cities)
  Breast Cancer Champions: Data to action

• Ceema Samimi, School of Social Work (Twin Cities)
  Critical Approaches to the Criminalization of Student Data

Perspectives on environmental sustainability with data

• Angie Gupta & Jeff Jackson, University of Minnesota Extension (Rochester & Cloquet)
  How to measure an event's carbon footprint

• Daniel Sandborn, Water Resources Science, Large Lakes Observatory (Duluth)
  What can Lake Superior tell us about local effects of global climate change?

• Stacey Stark & Zach Vavra, U-Spatial (Duluth)
  Leveraging national datasets for local Natural Hazard Mitigation Planning dashboards

"Data snacks": Data tools and skills sharing

• Peter Dolan, Computer Science & Statistics (Morris)
  A Brief Introduction to the Grammar of Graphics

• Marten Thompson, School of Statistics (Twin Cities)
  PINN-Point: Improving Machine Learning with Physics

• Amy Riegelman, University Libraries (Twin Cities)
  "Thwart the HARK!": Pre-registration for research

• Alex W. Bajcz, Minnesota Aquatic Invasive Species Research Center (MAISRC) (Twin Cities)
  Thinking Inside the Polygon— Using R to Better Understand Distances Inside Lakes (or Any Other Spaces With Goofy Shapes)

• Mike Beckstrand, Liberal Arts Technologies and Innovation Services (Twin Cities)
  Tools and Strategies for Qualitative Data Analysis

U of M Day of Data 2022

Data (science) for all

• Lana Yarosh, Department of Computer Science & Engineering (Twin Cities)
  CrashCourse Artificial Intelligence: AI / ML for the Masses

• Jong-Min Kim, Statistics (Morris)
  Movie Title Keywords: A Text Mining and Exploratory Factor Analysis of Popular Movies in the United States and China

• Will Bartsch, Kris Johnson, Natural Resources Research Institute (Duluth)
  MNAtlas.org – Free and Easy GIS

Data inside/outside the classroom

• Tori Hill, Crookston alum

• Ken Okeke, Crookston / Duluth alum

• Michelle Hum, Twin Cities alum

• Vipin Kumar
  Regents Professor, Department of Computer Science and Engineering (Twin Cities)

• Xavier Prat-Resina
  Associate Professor of Chemistry, Center for Learning Innovation (Rochester)

Algorithmic bias and data ethics

• Yuming Fang, School of Journalism and Mass Communication (Twin Cities)
  Empowered Social Bots

• Shanda Hunt, UMN Libraries (Twin Cities)
  Conducting research through an anti-racism lens: a library guide

• Ben Wiggins, UMN Libraries (Twin Cities)
  A Brief History of the Long History of Racial Bias in Algorithms

U of M Day of Data 2021

Session Recordings

"Data matters": Epidemiology/Health

• A Machine Learning Framework for Predicting Long-Term Graft Survival after Kidney Transplantation - Abdulaziz Ahmed, Business Department (Crookston)

• The 2020 Race/Ethnicity Aha Moment: The Wokeness of Cherry-Picking Variables in Health Disparity Research - Kristin Osiecki, Center for Learning Innovation (Rochester)

• University of Minnesota COVID-19 Hospitalization Tracking Project - Pinar Karaca Mandic, Carlson School of Management (Twin Cities)

"Data matters": Climate

• Using Data to Improve Campus and Community Sustainability Performance - Troy Goodnough, Office of Sustainability (Morris)

• Conservation drainage research: Leveraging long-term historical data and the Minnesota Phosphorus Index for water quality improvement - Lindsay Pease, Northwest Research and Outreach Center (Crookston)

• Climate, its changes and consequences: Discovering science with data - Ansu Chatterjee, School of Statistics (Twin Cities)

"Data matters": Social Justice

• Quantifying the relationship between census demographics and social distancing patterns in Minneapolis to foster awareness of the inequities associated with COVID-19 prevention - Emily Cavazos & Michael Felzan, Geographic Information Science (Twin Cities)

• Police Contact as a Determinant of Structural Racism: An Exploration of the Association between Preterm Birth in Neighborhoods with High Police Contact in Minneapolis - Tongtan Chantarat, School of Public Health (Twin Cities)

• News, Protests and Social Media Megaphones - Danielle Kilgo, School of Journalism and Mass Communication (Twin Cities)

Data Visualization Challenge

• Differential Privacy and the 2020 US Census - David Van Riper, IPUMS

• Data Visualization Challenge - Kari Williams, IPUMS

U of M Day of Data 2020

Streamed Session Recordings

Safer Statistical Significance (Until We Agree on Something Better)  - Ethan Brown & Michael Huberty, College of Education & Human Development

Why are statisticians saying we should stop using the term "statistical significance"? What can we actually do instead?  We will provide hands-on activities to help understand the concept of statistical significance, its history and pros/cons, as well as practical tips on what you can do now to improve your research & statistical practice in the midst of the growing open science movement. 

Thinking Spatially with R  - Travis Ormsby & Brittany Krzyzanowski, U-Spatial

An introduction to spatially aware analytical methods that showcase how you can get into trouble if you aren't thinking spatially. No background in spatial data necessary--all are welcome!

Scraping PDFs with RegEx and R  - Yeng Miller-Chang, Academic Health Center 

Have a data set that is only available in .pdf format? In this session, you will be introduced to the tools necessary to get your .pdf data in a usable format using regular expressions and R. Familarity with basic ideas of automated data analysis (e.g., data frames). Some familiarity with RegEx is helpful to have to get the most out of the presentation, but not necessary.  Software required: R, RStudio, web browser, and some PDF viewer

Introduction to Social Network Analysis with Python - Russell Funk, Carlson School of Management

This session will give an overview of social network analysis using Python. We'll cover a lot of ground in a short period of time, including basic network data structures, measures, and visualization. Along the way, we'll also conduct a network analysis of more than 30 Shakespearian plays, beginning with raw data scraped from the internet and ending with a few insights of potential substantive interest.

U of M Day of Data 2019

Streamed Session Recordings

Data and Ethics in Higher Ed - Colin DeLong

As the use of big data analytics and machine learning has worked its way into numerous higher ed technology platforms, conversations about the ethical use of student information lag behind. In this talk, we will walk through several examples of "big data" issues, possible solutions, and next steps.

Introduction to Programming Languages and Concepts - Michael Berkowski

We will explore several different types of programming languages, where you might encounter them, and why you might choose one over another, followed by a hands-on overview of the fundamental building block concepts common to all programming languages.

Signal Driven Data Curation and Commercialization - Ted Steinmann, Penny Norquist

Leveraging open access data signals, tools and alerts, the Food Protection and Defense Institute collaborates with students at UMN to identify and validate incidents of economically motivated and intentional adulteration incidents. These resources are compiled in database managed by FPDI, presented to users in a search interface and Tableau dashboard, and made commercially available via FPDI's University website.

U of M Day of Data 2018

Streamed Session Recordings

IPUMS Data Exploration - Greg Freedman Ellis, Erin Meyer, Kari Williams

IPUMS provides census and survey data from around the world integrated across time and space. All data are available free of charge. This data jam session will provide a brief overview of why IPUMS data are so great, introduce data enthusiasts to the data we make available, and offer participants the opportunity to analyze some of the various datasets we provide.

Data Storytelling with ArcGIS Story Maps - Melinda Kernik & Kate Carlson

Story Maps make it easy to integrate maps with text, charts, images, and other media in order to tell compelling narratives.  During this data jam session, participants will learn how to make a story map using ArcGIS Online, including techniques for finding/creating map content and tips for designing visually appealing maps.

"Rescuing" Data - Alicia Kubas & Shannon Farrell 

Federal government data is used every day by thousands of individuals for academic research, publishing and news, general consumer information, and so much more. With the US government being the largest producer of data in the world, access to this data is tantamount, but it's disappearing from the web for a variety of reasons. Learn about one way libraries have been trying to tackle this issue through the grassroots DataRefuge initiative and its associated DataRescue events.  Software needed: No software needed, but participants will need access to internet/laptop/etc.