The rapid advancement of technologies such as artificial intelligence, big data, and the Internet of Things has reshaped modern society into an environment where large volumes of information must be processed in real time. As a result, data literacy and informationprocessing competencies have become indispensable for students. Science education is well positioned to cultivate these abilities because its inquiry processes—observation, data collection, analysis, and interpretation—closely parallel the steps involved in data analysis. To address these limitations, this study developed a science inquiry program utilizing publicly available COVID-19 datasets, a topic closely linked to students’ everyday experiences, and implemented it with 125 second-year high school students in D Metropolitan City. The three-hour instructional program comprised five stages: (1) understanding big data, (2) identifying problems, (3) planning data collection, (4) analyzing and visualizing data, and (5) drawing conclusions and presenting findings. The big data analysis activities were conducted individually, and students freely described their work in Google Slides submitted through Google Classroom. The effectiveness of the classes was evaluated through data literacy assessments and class satisfaction surveys. The findings show that the big-databased inquiry activity using public data had a positive impact on students’ data literacy and satisfaction with the class. Additionally, the results underscore the importance of continuous teacher guidance and feedback throughout the inquiry process. Based on these outcomes, this study highlights the need for future science instruction to incorporate not only quantitative but also qualitative approaches to big data analysis.