Using Data Science to Create an Impact on a City Life and to Encourage Students from Underserved Communities to Get into STEM

Abstract

In this paper, we introduce a novel methodology for teaching Data Science courses at New York City College of Technology, CUNY (CityTech). This methodology has been designed to engage our diverse student body. CityTech 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 New York City communities that are underrepresented in the STEM fields and at the decision-making positions in the government (at the city level, state level, country level). However, our methodology flips 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 uses case-based learning and diversity among our students who come from different city communities (location-wise, ethnicity-wise, income-wise) to enrich and drive the education experiences. 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 ran a pilot study within one introductory class designed specifically for the BS in Data Science program. In this pilot study, we taught data analysis utilizing data sets collected by the New York City agencies. Our findings demonstrate that using real-life data sets encourages students to compare the results learned from data about their communities and their everyday experiences. We believe that using such a teaching approach can be a great start for igniting the interest in the field as well as in society-aware aspects of data analysis.