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College-bound students Decision Support with Data Science

Jupyter Notebook 100.00%
colleges-information data-analysis data-analysis-python data-science data-visualization sat

college-bound's Introduction

Introduction

As a part-time tutor who also dabbles in data science, I have several students who are struggling to understand what the future holds for them and how standardized tests fit into their life path. They are feeling overwhelmed with all the data around "acceptable test scores" for various schools and majors. They want a good education, but need some help narrowing down the choices for majors and schools, based on their target (or expected) SAT score range. The purpose of this project is to help explore existing score data and how it relates to various majors and colleges for college-bound students.

Intended Major by Math Example Image

SAT Score Acceptance Rates

How to Run:

This is a simple jupyter notebook that contains various steps to clean SAT, ACT, College, and College Major data that produces handy visualizations as well as cleaned dataframes that can be used to customize your own visualization. Simply run the notebook with your favorite .ipynb tool and you should be able to produce the results shown in the images folder.

Discussion and Next Steps

Based on all this data, we can conclude that being able to look at the subscores and SAT Quartiles of scores that correspond to certain majors or colleges is a useful way to approach becoming informed about the process. By sorting our score values we were able to understand which were the majors that correspond to which of the top scores. For example, it helps to know that if I want to pursue an engineering degree, I will probably want to make sure to have a high math score, but also a high reading and writing score (as opposed to the conventional logic of just needing math). Future projects could include building a machine learning recommender system that allows one to take all of these colleges with a corresponding score and recommending automatically the best college/major for a given student's score.

Data Sources

There are 10 datasets included in the data folder for this project.

Data Dictionary

Feature Type Dataset Description
intended_major object 2019 SAT Scores by Intended College Major The name of various majors and fields of interest for prospective college-bound applicants.
test_takers_per_major float 2019 SAT Scores by Intended College Major The number of test takers who are interested in major
percent_of_total_decimal float 2019 SAT Scores by Intended College Major Percent of total expressed as a decimal
total_accepted int 2019 SAT Scores by Intended College Major Total number of students accepted for the intended major
reading_writing_sat_score int 2019 SAT Scores by Intended College Major Score for the reading and writing SAT section
math_sat_score int 2019 SAT Scores by Intended College Major Score for the reading and writing Math section
Feature Type Dataset Description
school_name obj Ranges of Accepted ACT & SAT Student Scores by Colleges Name of the University or College
number_applicants int Ranges of Accepted ACT & SAT Student Scores by Colleges Number of applicants for this college
acceptance_rate float64 Ranges of Accepted ACT & SAT Student Scores by Colleges Acceptance rate as a decimal
sat_quartile_range object Ranges of Accepted ACT & SAT Student Scores by Colleges SAT Quartile range (as a string)
act_quartile_range object Ranges of Accepted ACT & SAT Student Scores by Colleges ACT Quartile Range (as a string
sat_lower_quartile float64 Ranges of Accepted ACT & SAT Student Scores by Colleges SAT Score marking the lower quartile for the given college or university
sat_upper_quartile float64 Ranges of Accepted ACT & SAT Student Scores by Colleges SAT Score marking the upper quartile for the given college or university

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