Data4All High School Bridge Workshop

4 students sitting around a table working together on their laptops and smiling.

The application for the Fall 2024 Data4All program is now closed. Applications for the Spring 2025 iteration will be available around late January – early February of 2025.

Data4All engages high school students in high-level data and coding challenges. Our goal is to create a continuous, evolving computer science pathway for students, allowing them to smoothly transition from the basics of computing through high school to college and beyond.

Activities at the workshop include:

Learning the Python programming language (no prior knowledge of Python necessary)
Conducting inquiry-driven research in small groups
Training on how to investigate data problems and present findings
Developing critical collaboration, problem-solving, and communication skills

Data4All is open to high school sophomores, juniors, and seniors who have completed Algebra 1. The program lasts 8 weeks, with students meeting on Saturdays at the University of Chicago campus.

WORKSHOP OVERVIEW

The Data4All High School Bridge Workshop was developed to serve as a transition from introductory computer science classes to data science research. The workshop introduces students to the data science research lifecycle, essential computational skills needed for data analysis and visualization, and provides training on how to communicate their findings. The workshop focuses on creating a continuous learning environment from students’ structured classroom studies to more experimental, inquiry-driven research work in small groups. Read more about the March 2021 inaugural workshop here.

Two students sit next to one another. Both are looking at a laptop, and one is pointing to something on the screen.

MOTIVATION

In most Chicago Public High Schools (CPS), students’ exposure to computer science is limited to one computer science survey course, with limited exposure to advanced topics such as artificial intelligence, data science, or application of computer science to societal issues. This lack of opportunity continues to be perpetuated when students seek internships and other employment experiences and do not have the confidence in their own knowledge to see computer science, data science or artificial intelligence as a possible career pathway for themselves. To address this need, a team of researchers and educators from Argonne National Laboratory and the University of Chicago developed a data science bridge workshop that supports students from Chicago’s South Side community to develop a deeper understanding of data science and grow a tangible skill set that is grounded in scientific projects, real-world datasets, and professional tools. Through this program students will explore the foundational concepts of computer science and data science, working with authentic and complex datasets and leveraging principles of AI to gain insights from data and make predictions.

WORKSHOP FOCUS & APPROACH

The workshop is taught by using case studies that contain a real-world scientific challenge (i.e. COVID-19), an authentic data set and associated professional tools. The case studies are supplied from data generated by scientific research projects from the three institutions. Using the Python language, students explore data structures with an emphasis on multidimensional arrays, manipulating and visualizing them with commonly used libraries in scientific computing such as NumPy, Pandas and Matplotlib. The datasets provide students with many of the challenges associated with scientific data and provide them with the skills to perform statistical analysis and prediction all the while exploring real-world problems.

The workshop models the collaborative nature of computer science by situating students in teams with guidance and support of staff, including undergraduate and graduate mentors from the three institutions.

Funding for the workshop is supported by a grant awarded by UChicago’s Office of Research and National Laboratories Joint Task Force Initiative as well as by a grant from the Successful Pathways from School to Work initiative of the University of Chicago, funded by the Hymen Milgrom Supporting Organization. Previous collaborators for the workshop include the University of Chicago’s Office of Civic Engagement and the Center for Spatial Data Science, as well as Argonne National Laboratory.

Team

Evelyn Campbell is the Program Manger for Community-Centered Data Science at the Data Science Institute. She oversees and implements the DSI’s educational outreach programs, such as Data4All and the Data Science for Social Impact Research Experience. She was previously a Data Science Preceptor where she taught data science curriculum for both the University of Chicago and City Colleges of Chicago. She obtained her PhD in Microbiology from the University of Chicago in 2022 and her BS in Biology from Rider University in 2016. She is an advocate for educational access and expanding representation in data science and other STEM fields.

Julia Koschinsky is the Executive Director of the Center for Spatial Data Science at the University of Chicago and has been part of the GeoDa team for over 16 years. She has been conducting and managing research funded through federal awards of over $8 million to gain insights from the spatial dimensions of urban challenges in housing, health, and the built environment.

David Uminsky joined the University of Chicago in September 2020 as a senior research associate and Executive Director of Data Science. He was previously an associate professor of Mathematics and Executive Director of the Data Institute at University of San Francisco (USF). His research interests are in machine learning, signal processing, pattern formation, and dynamical systems. David is an associate editor of the Harvard Data Science Review. He was selected in 2015 by the National Academy of Sciences as a Kavli Frontiers of Science Fellow. He is also the founding Director of the BS in Data Science at USF and served as Director of the MS in Data Science program from 2014-2019. During the summer of 2018, David served as the Director of Research for the Mathematical Science Research Institute Undergrad Program on the topic of Mathematical Data Science.

Before joining USF he was a combined NSF and UC President’s Fellow at UCLA, where he was awarded the Chancellor’s Award for outstanding postdoctoral research. He holds a Ph.D. in Mathematics from Boston University and a BS in Mathematics from Harvey Mudd College.