Understanding data analysis and interpreting data are key components of teaching interdisciplinary undergraduate students. We detail a semester-long research project that introduces students to long-term data sets, incorporates the use of widely available statistical analysis, and underscores an inquiry-based method of teaching climate change. Our learning objectives included analysis of large-scale climate data and interpretation of empirical trends in the context of two climate change phenomena: warming and the urban heat island effect. We demonstrate how small groups of students (n = 2 or 3) were empowered to independently download and analyze long-term temperature data to be aggregated into a class data set. Students used the National Centers for Environmental Information (NCEI) to find the long-term data sets of hourly maximum (TMAX) and minimum (TMIN) temperatures. Students used the open-source statistical software R to manage and summarize data. Students also examined changes in land use cover using Google Earth satellite data to quantify whether stations were urban or rural. Students were assessed in their groups based on a research paper and a class presentation. Approaching climate change education from the inquiry-based learning perspective allows students to understand how scientific research is conducted, apply the scientific method, and experience firsthand the importance of open-source data.