Unique Student Count: Determine the number of distinct students within the dataset. Average GPA: Compute the mean Grade Point Average (GPA) of students. Graduation Year Distribution: Analyze the distribution of students across different graduation years. Python Experience Distribution: Explore the prevalence of student experience with Python programming. Average Family Income: Calculate the mean family income of students. Top College GPAs: Identify the variation in GPA across the top 5 colleges.

City-wise GPA: Determine the average GPA for students from each city. Income-GPA Relationship: Investigate the correlation between family income and GPA, understanding the potential link between financial background and academic performance. Investigate the relationship between 'GPA', 'Family income', and 'Experience with Python (Months)' with respect to expected salary variations. Analyze which specific fields of study attract more students to particular events. Examine whether students in leadership roles during college exhibit higher GPAs or expected salaries. Explore whether there's a correlation between students' leadership skills and their expected salaries. Determine the count of students expected to graduate by the end of 2024. Identify the most effective promotion channel driving student participation in events. Calculate the total number of students attending Data Science-related events across all relevant courses. Compare the correlation between high CGPA and language experience with high salary expectations (average). Quantify the number of students informed about the event through their colleges, focusing on the top 5 colleges.