1. Dataset Overview:
This dataset contains information on student performance across various subjects, including Math, Reading, and Writing. In addition to scores, the data provides insights into several demographic and circumstantial factors, such as gender, race/ethnicity, parental level of education, lunch type (free/reduced or standard), and test preparation course completion. This dataset allows us to explore the relationship between these factors and student academic performance.
2. Research Questions:
This dataset presents several compelling questions about student achievement. Does parental background, specifically the level of education, influence their children's scores? Does participation in a test preparation course significantly improve performance? Are there disparities in academic outcomes across different racial/ethnic groups or genders? Does lunch type, which partially reflects socioeconomic status, correlate with student scores? What drives student performance in each specific subject? And are there correlations between scores across different subjects? We will explore these questions through data analysis and visualization. Can we identify key factors that predict student success?
3. Visualization: