The Capacity Accelerator Network is building a workforce of purpose-driven data practitioners worldwide and enabling social impact organizations to unlock the power of data to meet their missions. Dr. Amanda R. Kube Jotte is a Preceptor in Data Science at The University of Chicago, and through the US Capacity Accelerator Network, she is on a mission to make data science education more accessible to students.

Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by?

I am thrilled to have the opportunity to work with students from the Capacity Accelerator Network for the third consecutive year. As part of the Data Science for Social Impact Summer Experience, I teach a data science “crash course” and mentor groups of CAN students on real-world projects. In 2022, I worked with a group of students to analyze traffic stop data for Business Professionals for the Public Interest who work to improve legislation around biased police practices. Last summer, my students and I analyzed geospatial data capturing where different pesticides are applied for Californians for Pesticide Reform. Through our work, students see how data science can be applied to do good in their community.

Working with CAN students has been incredibly fulfilling for me. Witnessing their growth as aspiring data scientists throughout the program is truly rewarding. They learn a lot of material at a very quick pace, not only through formal lessons but through the experiential learning involved in their research project. The program is meant to jumpstart interested students’ data science careers. This is really important since we need more data scientists with diverse backgrounds and experiences. I have personally seen and experienced how crucial it is to feel represented and heard in your field. That’s why I am most excited when I see an increase in their confidence in their abilities and their sense of belonging in this field as the program progresses.

What are some of the challenges of doing this work? Which were anticipated, and which unexpected?

Developing a curriculum that takes into consideration the differing levels of prior knowledge among students and aims to foster equal opportunities for learning has been one of the major challenges of this work. Data science is an interdisciplinary field, incorporating elements of statistics, computer science, and research methodology, and students often have varying levels of experience with these different aspects. For instance, some students are highly skilled in mathematics but have no experience in coding, while others are proficient coders but have never taken a statistics course. And some students may not have had the opportunity to be exposed to much of either. This challenge is also present in my work at both the University of Chicago and the City Colleges of Chicago. The introductory data science material must meet a variety of needs to ensure that students are well-prepared for the next stage, whether that be a research project or a course in advanced machine learning. 

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

My teaching approach has certainly developed through my involvement with CAN. I’ve learned a great deal from conversations with faculty members at Truman College, especially about how to effectively teach students from different educational backgrounds. Talking with educators like Kate Connor has been really valuable—it’s given me a sense of support and guidance as I navigate my role as a young professor.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

This is a very important question, and it’s been a topic of intense discussion among members of the data science community. Drawing from my education and personal experiences, I believe it’s crucial to involve social impact organizations and the people they work with in every step of our projects. By keeping these organizations meaningfully informed and involved, we not only enhance transparency but also foster a sense of ownership, which is fundamental for building trust and increasing adoption. After all, these tools are being designed for use by these organizations, so it really makes sense to solicit their input for the design process. This collaborative approach also ensures that we are tackling the most pressing issues as identified by those directly impacted, leveraging their domain expertise to keep our work both relevant and enduring. 

During the CAN DSSI Summer Experience, we emphasize the importance of this collaboration, and students regularly engage with organization members through Zoom calls to present their progress. This model mirrors our approach at the University of Chicago in the DSI Clinic (an experiential learning opportunity for data science majors), where students routinely cite their interactions with clients as one of the most impactful parts of the experience.

The US CAN playbook Data Science for Social Impact in Higher Education: First Steps, provides educators with a range of ways to bring data for social impact to students. The playbook also includes ways to embrace social impact and ethics, elevate support and reduce barriers, and engage partners. How are these topics useful as you bring the UChicago DSI course to City Colleges of Chicago?

These topics are a key component of my curriculum planning for the data science sequence at City Colleges of Chicago. Part of the motivation for bringing these courses to the City Colleges is to make data science education more accessible to students. During the courses, we have built up a strong support network for students including lab sessions, office hours, and discussion boards. I also encourage peer support and community building by promoting an environment where students can collaborate, answer each other’s questions, and talk about common concerns or sources of confusion. In the classroom, I work to build a community where students feel comfortable asking questions, commenting on material, and connecting what they learn to their own experiences. We discuss ethics and social impact using case studies and topical data sets. For example, we analyze data on bias in policing and trends in college admissions. We also take time to explore datasets that are more lighthearted yet still relevant to students, such as Spotify listenership. Through these discussions, we aim to maintain an atmosphere of curiosity as well as mutual respect and support. This approach helps students engage with and learn from difficult social impact and ethical questions. I have found that this approach motivates students to question and explore data beyond what may be asked in an assignment.