Mathematics student at Fresno State who participated in the Data Science in Our Community module
California State University, Fresno (Fresno State)
Inspiration to start the module
The mathematics and computer science courses at Fresno State cover a breadth of topics; however, very few of them connect how applied mathematics and data science could be used to address community issues. During the summer of 2021, with the support of an NSF-funded IDEAS Lab, Dr. Mario Bañuelos collaborated with an ethnic studies lecturer at California State University, Stanislaus, Christina Acosta. Each person is guest-lectured in the other’s courses. Dr. Banuelos connected computational tools and methods to ethnic studies topics and Christina Acosta provided historical and sociological context to both a mathematical modeling and an introduction to biostatistics course. These led to fruitful discussions about topics such as Native American land and the long-term effects of redlining in the United States.
These conversations and exchanges resulted in two goals: 1) having this content added to the data science module, and 2) publishing this framework of a one-week exchange between ethnic studies and a mathematics instructor.
Course Example
This module, currently titled “Data Science in Our Community,” focuses on connecting data science and mathematical approaches to understanding how algorithms and data affect the everyday lives of students. More specifically, it focuses on a general introduction to data ethics as well as using maps to highlight inequities and begin discussions on understanding how communities of color are negatively impacted. For courses that are less focused on computation, there are opportunities for students to engage in connecting data to their current course material. In contrast, for courses that primarily focus on programming, mathematics, or statistics, there are pages, videos, and tasks related to connecting the context of their course to topics within ethnic studies.
Support: Buy in at the college level and from the dean to move forward with a transdisciplinary module related to data science as well as funding from data.org to provide time to organize and pilot it in a mathematical modeling course.
Barrier: While creating this module as well as developing the Applied Data Science Minor, administration has been supportive of an increased data science culture and programs at the university, but getting input across many departments takes time and personnel.
At this point, the next steps are to publish and disseminate the module on Canvas Commons. The vision for this module is for it to be continually updated benefitting the instructors including it in their courses.
Three considerations to determine next steps:
Publish and disseminate module on Canvas Commons, advertise widely at Fresno State.
Set up a professional development opportunity on how to use the “Data Science in Our Community” module in a variety of courses.
Solicit faculty feedback on content and revisions as necessary.
Creating a module required time and input from multiple perspectives. It would be pertinent to advocate for faculty input and content throughout the development process.
A-ha Moment
At an institution where there are a lot of departments covering similar content, creating a transdisciplinary module has provided opportunities to connect those topics to relevant questions surrounding the community.
Approach to Social Impact
Engaging communities in ways that empower the community to use their own data for purposes they co-define.
Encouraging students to seek, explore and analyze data sets that surface issues of social impact.
At all stages of work considering the potential social impact of that work.
Building open science principles into courses.
Incorporating accessibility practices into teaching and learning.
Undergraduate student in Applied Mathematics at NC State who completed the Internships for Social Good course.
North Carolina State University
Story of the course
At NC State University, we created a course called Data Science for Social Impact for the Data Science Academy (DSA). The DSA is a unit in the Office of University Interdisciplinary Programs in the Provost’s Office. The goal of the DSA is to catalyze and network data science across the entire university. DSA 1 credit courses are project-based and developed using the principles of our ADAPT model. They are open to all students, faculty, and staff as well as non-degree students. In just two years, DSA courses have attracted students from all the colleges of the university and 100 majors. The demographics of students on the courses overall mimic the demographics of the university.
This new course developed as a result of this grant originated from a desire to prepare students for internships without being responsible for placing them through our career fair.
In order to attract students who already had some data science experience, we numbered the course as an intermediate-level undergraduate course and made a decision to not make it accessible to graduate students. We might change that in the future since graduate students also seem interested in internship preparation.
This project has sparked a new collaboration between the NC State Data Science Academy and the RuralWorks program that will help social impact organizations use their data in new and more effective ways
Rachel Levy, PhD, Executive Director
To teach the course, we identified an instructor who was a professor in the College of Humanities and Social Sciences in the School of Public Policy and International Affairs, Dr. Tracy Appling. While data science was a new area for her, she had decades of experience preparing students for internships. She was the primary instructor of record and we paired her with a faculty member from the mathematics department, Dr. Hangjie Ji, who had extensive experience with industrial mathematics, including developing and leading workshops for graduate students to prepare them for intensive week-long workshops in which faculty gather to solve problems from industry.
A wonderful unanticipated outcome of the course and of our career expo is that we were able to combine efforts with an existing program called Rural Works, placing undergraduate students in paid internships. By making some guidance available from graduate students, we were able to help students infuse data components into those internships. This idea came up because our social impact table with Dr. Tracy Appling at our career fair inspired our career services director Dr. Kelly Laraway who ran Rural Works to realize the concepts could be combined. This allowed us to both leverage and enhance an existing program – a real win-win.
Inspiration to start the course
We were inspired to develop Data Science for Social Impact by student comments that they were not aware that data science could be used for social impact. We realized that most students hear about opportunities to apply data science in the tech sector, but may not learn about opportunities in nonprofits, government, or community organizations.
At the time, as a new unit of the university, we were not ready to set up a full internship program ourselves. Yet we were hearing that students could benefit from some experiences that would enable them to bring their data skills into an internship. We also hoped that such a course might open students’ thinking to social impact internships in small businesses, non-profits, and government in addition to doing internships in more typical tech spaces.
Course Example
Data Science for Social Impact is a one credit course that meets 50 minutes once a week and as noted above, is designed in alignment with the Data Science Academy ADAPT model.
During the first semester of the course, we offered both an in-person and an online version. The next offering, from a different instructor, was in person only, based on her preference. We are open to online versions in the future.
Overview: In this course, students learn about applying for internships for social impact in nonprofit, governmental, and community organizations. As part of this preparation, students become familiar with tools (such as a data maturity questionnaire) that can help organizations assess their own use of data. Students are encouraged to use these tools and assessment results to initiate conversations about the organization’s data practices and goals. Students learn about the appropriate scope of projects for an internship. They practice basic data management, analysis, and visualization through a mini-project using data from a real organization that has a focus on social impact. Additional emphases include developing and refining interviewing skills, building professional and personal networks, completing job applications, and engaging in job selection.
Prerequisite skills: Some elementary data science experience that could be applied in an internship.
Learning Outcomes. By the end of the course, students are able to:
Summarize their data science skill set in relation to an organization’s needs;
Evaluate their technical and applied data skills;
Communicate data analysis and recommendations while considering social implications;
Produce a career readiness portfolio;
Apply appropriate principles to job search preparation; and
Identify key resources within the job search process.
Support: The pairing of the two faculty members with complementary expertise.
Support: The prior relationships of the faculty member with alumni who had founded social good organizations. We met with several alumni and settled on one who could propose a project and provide data.
Support: A student from the first year’s Data Science Social Impact (DSSI) summer program who was interested in serving as a TA in the course.
Support: Funding from data.org to motivate us to try this new course.
Barrier: Needed to work out a data confidentiality agreement with the organization supplying data and the problem to solve.
Barrier: Needed to figure out how to transfer, organize and store the data to prepare it for student use.
Barrier: Low student enrollment because in the first two offerings, the course didn’t yet “count” for anything. In the future, it will fulfill the requirements for a data science minor and certificate.
Finding successful ways to encourage students to enroll. We think the inclusion of the course as a way to satisfy a requirement for minors and certificates will attract more students. At some point, we may need to offer multiple sections to satisfy demand.
Further explore the differences between online and in-person sections.
Think about student interests to help prioritize which organizations to serve during the course.
Make sure that you have ways to create or build on existing relationships with social good organizations. They likely will need to trust you before they share data and it may take time to build that trust.
A-ha Moment
Students are not aware of the ways that attention to data can really help non-profits and other social impact organizations.
Approach to Social Impact
Recruiting and supporting students from groups underrepresented in data science.
Engaging communities in ways that empower the community to use their own data for purposes they co-define.
Choosing course topics with titles and topics that directly point to social impact.
Encouraging students to seek, explore and analyze data sets that surface issues of social impact.
At all stages of work considering the potential social impact of that work.
Incorporating accessibility practices into teaching and learning.
Designing educational experiences intentionally and collaboratively to build equitable and accessible opportunities and pathways in data science.
Providing students with work-based learning and awareness of career opportunities.
Undergraduate in Applied Statistics major at University of Illinois Chicago who completed the experimental module in STAT 385: Elementary Statistical Techniques for Machine Learning and Big Data
University of Illinois Chicago
Inspiration to start the course
In the Fall semester of 2021, UIC started offering a new data science major for undergraduate students with core courses from computer science, mathematics, and statistics, and concentrations in areas such as business, communication, bioinformatics, health, and public policy. This program has experienced considerable growth, particularly within the computer science concentration. However, there has been limited enrollment for the other concentrations. To broaden the appeal of data science to a wider range of students and bolster enrollment in less-represented concentrations, we decided to develop an introductory experiential learning course in data science. This strategic initiative aims to attract a more diverse student body and cultivate interest in various data science concentrations.
Course Example
This course is an introductory 100-level experiential learning course in data science. It is designed to be accessible to all undergraduate students at UIC, irrespective of their background in statistics or computer science. Its primary objective is to engage students’ interest in data science and inspire them to explore career opportunities in this burgeoning field.
The course introduces students to the fundamentals of data science, imparting essential skills for data exploration, visualization, and modeling. Central to the curriculum is a semester-long social impact data science project, providing students with hands-on experience in applying these skills to real-world problems. Furthermore, the course introduces basic statistical concepts and programming skills. It also serves as a gateway to the data science major at UIC, shedding light on the diverse applications of data science, with a strong focus on those with significant social impact. By highlighting potential career pathways in data science, we aim to inspire and guide students towards a future in this dynamic and rewarding field.
Support: We initiated discussions with administrators from the Department of Computer Science and the Department of Mathematics, Statistics, and Computer Science at UIC to lay the groundwork for the new data science course. These meetings yielded consistent support from both departments for the course’s development. We plan to initially offer the course as a general “special topics” course accessible to undergraduate students and to cross-list it as both a Computer Science and a Statistics course. Depending on the outcome of the course, it may then become a permanent offering.
Support: Within the Department of Computer Science, an existing “special topics” course will accommodate the initial offering. However, no such course existed in Statistics. To facilitate cross-listing, we engaged in discussions with the Director of Undergraduate Studies and the Associate Head of Instruction in the Department of Mathematics, Statistics, and Computer Science. We received substantial backing from the Associate Head of Instruction and later the Department Head, marking the first step toward creating the much-needed “special topics” course in Statistics. The course proposal was ultimately approved by the College of Liberal Arts and Sciences for the Spring 2024 semester.
Strategy: Prior to offering the new course, we conducted an experimental module within an existing data science course, STAT 385: Elementary Statistical Techniques for Machine Learning and Big Data. In this experiment, we introduced a social impact data science project designed to enhance traffic safety in the Chicago area. This project spanned approximately two months, from March 2023 to May 2023, and involved the analysis of over 700,000 traffic crash records from the Chicago Data Portal. Students were tasked with applying machine learning methods to identify the primary causes of traffic crashes and propose potential solutions to mitigate them. The class was divided into groups, and regular instructor meetings were held to monitor project progress. Ultimately, students submitted comprehensive reports and presented their key findings, culminating in a successful experiment.
Discussing the new course with administrators, including the format, schedule, and other teaching arrangements.
Advertising the new course to undergraduate students across the university to ensure enrollment.
Developing materials for the new course, including a semester-long social impact data science project.
Creating a new course needs tremendous support from the administration in the department and college. It also takes time to process the paperwork. It’s advisable to start conversations with administrators and colleagues as early as possible.
A-ha Moment
It was a surprise to find that there were no existing “special topics” courses for beginning undergraduate students in Statistics at UIC. It was a rewarding experience to create such a course.
Approach to Social Impact
Engaging communities in ways that empower the community to use their own data for purposes they co-define.
Choosing course topics with titles and topics that directly point to social impact.
Encouraging students to seek, explore and analyze data sets that surface issues of social impact.
Designing educational experiences intentionally and collaboratively to build equitable and accessible opportunities and pathways in data science.
Multidisciplinary Studies in Cyber Intelligence student who completed the Data Science and AI for All elective at University of Texas San Antonio
University of Texas at San Antonio
Inspiration to start the course
The inspiration for refining and extending our existing “Data Science and AI for All” course came from realizing that we could collaborate with like-minded universities and colleges who are working to increase open access to data science and AI learning.
The original course development was led by the UTSA “Generation AI Nexus” or “Gen AI” initiative, which has existed for the past six years. The aim is to help all students understand AI and how to use it as an effective tool. Under this initiative, faculty developed the course with five modules incorporating AI, machine learning, big data analytics, and data visualization. Upon completion of each module, students earn micro-credentials – in the form of digital badges. By earning a badge, which can be added to their portfolio, students are able to document their career development skills (and campus life involvement). They can also post them on LinkedIn, and other social media channels.
The additional development for the course, undertaken with CAN, focused on exploring the use of augmented reality (AR)/ virtual reality (VR), to make materials more accessible to students with disabilities. A primary source of inspiration to pursue access for students with special needs came from the long-term relationship that UTSA has with Morgan’s Wonderland and its Multi-Assistance Center.
Course Example
DS 1001. Data Science and AI for All. (1-0) 1 Credit Hour: is an eight hour-course designed for students from all academic backgrounds to develop interests in data science and artificial intelligence. It is an introduction to the concept of analyzing data culled from a variety of sources, and understanding the methods of aggregating data, forming coherent queries, and building machine learning models to derive insights from data. Topics may include Python programming using Jupyter Notebook, R programming, text analysis, database, data analytics, and data visualization. During the course development, no assumption was made regarding students previous experience with programming or math, therefore rendering the material more digestible and less intimidating. There is no prerequisite to enroll into this course.
One of the experiential learning modules focuses on accessibility for students with disabilities through the use of visual and interactive technologies such as extended reality (XR), VR, AR, which have seen a significant increase of use in recent years. Those technologies can greatly enhance learners’ experience of accessing the course module.
Barrier: Low level of student enrollment during the initial semester the course was made available. This is often the case with any kind of new program offering. A next step is to offer the course again this time with a focus on emerging technologies to increase accessibility for all.
Support: The relationship with faculty and the broader community was key during the module development because it ensured and increased the likelihood of Data Science adoption by students of all disciplines.
Support: One of the key ways to achieve student buy-in and enrollment are to leverage the university’s pathways through K-12 initiatives to foster a sense of curiosity and interest in data among first-year incoming freshmen.
It is a small component of the long-term goal of building a data science community across all disciplines at UTSA.
A-ha Moment
The timeline for approval to include the course in the university catalog is a lengthy process. It requires considerable effort. Start as early as possible.
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