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In this paper, we introduce a novel methodology for teaching Data Science. Our methodology relies on the outlook of the student body in our college. Our college is an urban, commuter, HSI (Hispanic Serving Institution) school with 34% Hispanic and 29% Black students. 61% of our students come from households with an income of less than $30,000+. Thus, many students in our college come from the communities that are underrepresented in the STEM fields and in the decision-making positions in the government (on the city level, state level, country level). However, in our methodology, we want to flip the situation so that our students’ living situation does not hold them back but on the contrary, gives them an edge in their education. Our methodology combines case-based learning and the diversity of our student body who come from different city communities (location-wise, ethnicity-wise, income-wise). We demonstrate that this combination can be the basis of a powerful teaching method that delivers STEM material and engaging students in the learning process.

To evaluate our novel methodology we run a pilot study within one of our introductory classes designed specifically for the BS in Data Science program. In this program, we teach data analysis utilizing the data sets collected by the city agencies. We demonstrate that using real-life data sets encourages our students to compare the results of what they learn from the data about their communities and their everyday experiences. We believe that using such teaching approach can be a great start for igniting the interest in the field as well as in society-aware aspects of data analysis.


Filatova, E., & Hecht, D. (2021, July), Using Data Science to Create an Impact on a City Life and to Encourage Students from Underserved Communities to Get into STEM Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. © 2021 American Society for Engineering Education.



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