College Exam Results (SAT)

College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010.

Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report.

Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school.

For data science, this dataset offers a rich source for exploratory data analysis, predictive modeling, and statistical testing. Researchers can explore correlations between SAT scores and other factors like school resources, student-teacher ratios, or geographic locations.

• Exploratory Data Analysis (EDA): Data scientists can use descriptive statistics and visualization techniques to understand the distribution of scores, check for outliers, and identify patterns or anomalies in the data.
• Predictive Modeling: Building models to predict SAT scores based on various predictors, such as school demographics or previous academic performance. This could include regression analysis or more complex machine learning algorithms.
• Time Series Analysis: If data across multiple years were available, analyzing trends over time would be possible, helping in understanding improvements or declines in performance.
  Comparative Analysis: Comparing scores across different schools or districts to evaluate disparities in educational achievement.
• Statistical Testing: Conducting hypothesis tests to see if the differences in performances across groups (e.g., by geographic region or school type) are statistically significant.