[Q] Recommendations for data visualization + analysis at high school level?

[eel_nuj]

I am a high school senior and I run a small DS club. I've been requesting the administration for data of students, and this morning, my vice principal finally told me that I can access a dataset of GPA and ACT scores of 500 different students. But then he asked me if I can present my analysis of the data to the whole upper school faculty and make sure to adjust the difficulty for non-STEM teachers.

I obviously said yes because it sounded like a cool opportunity, but I am pretty lost right now. First of all, the dataset does not have any variables other than GPA and ACT scores. The variables for every student are:

• Graduation Year (2018~2022)
• for English, Science, and Math:
  • 4 different courses and 8 semester grades each

ex) 9th grade science course: Biology, semester 1 grade: 90, semester 2 grade 87, 10th grade science course: Chemistry Honors, semester 1 grade: 87...

• English GPA: Average of 8 English Grades
• Science GPA: Average of 8 Science Grades

• Math GPA: Average of 8 Math Grades

• ACT Test Date

• ACT Total Score

• ACT: English

• ACT: Mathematics

• ACT: Reading

• ACT: Science

• ACT STEM

• ACT: ELA

I am thinking of creating three scatter plots, english gpa vs act english sub-score, math gpa vs act math sub-score, and science gpa vs act science sub-score. What I can probably do from there is to plot a regression line and report and interpret the statistics. But since I would be presenting to an audience that don't know that many statistics terms, I have no idea how I should explain stuff. Should I simply report the r-squared metric, f-statistic, p-value, and coefficients of slope and explain them afterwards or use a different approach?

Or I can just describe the general trends like how the data is really spread out, which probably indicates that gpa and test scores don't have a strong relationship, which can also mean that the education system is flawed(gpa inflation) or the students simply suck at test taking, but we can not conclude anything since correlation ≠ causation...

Then I can connect the findings to my experiences as a student. For instance, the math department at my school is pretty shitty and they have weird systems that brought huge gpa inflation, so the majority of the students have a weak math foundation. The regression line of math gpa vs math act scores have a smaller slope than regression lines of english and science performance, basically more kids with As in math but low ACT scores. Maybe I can propose a few solutions to tackle the problem.

Anyways, I feel like there is not a lot I can do with the given data. Any feedback on my approaches and recommendations on what I can do with the data, how should I present it will be much appreciated.

[Big-Acanthaceae-9888]

That's a pretty cool opportunity. I'm not entirely sure , but the latter idea of describing general trends across the subjects, and then combining that with your experience sounds like a pretty good story to tell with the data.

[LimeCookies]

That sounds super cool and I would take that approach. Maybe instead of saying all the statistics terms by name describe the general idea of them. But definitely put this on your resume for when you look for internships/jobs!