Inspiration
As two aspiring, incoming seniors that are navigating their way through the college application process, we know how tough it is to find the ideal university - especially when you are targeting such a competitive field such as computer science. We wanted to make it easier on us, as well as up and coming high school students to find their ideal university. There are so many universities that come with pro’s and con’s, and with such an expanded array of options, we want UniFinder to help condense the long, strenuous process.
What it does
UniFinder is a Python based project that makes it easier for high school students to find their ideal university. Our platform asks for users to input their fundamental educational demographics such as weighted and unweighted GPA, SAT/ACT scores, AP Exam Scores, and Volunteering Hours. In addition, we are able to provide users with information on their ideal colleges - whether it's regarding the environment, community, or educational standards. Our goal is to make it easier for students to find their most compatible college using their academic and extracurricular information.
How we built it
We built the basis of our code using Python. We used arrays to organize the data we collected - one set of arrays for each major (Computer Science, Business, and Medicine). Each set of arrays consists of data that corresponds to various statistics - average admission GPA, average admission SAT & ACT scores, and average admission AP Exam scores that correspond to the major. For example, for Computer Science, the AP Exams we would have statistics for would be AP Computer Science A, AP Computer Science Principles, AP Calculus AB, AP Calculus BC, and AP Physics. We then put together if-else and for loops where we defined different commands, such as entering weighted and unweighted GPA, and different selections, such as Calculate, Volunteer, etc. This would allow the user to input their personalized information while simultaneously corresponding to their major - their major selection would then pull data from one of the three sets of arrays.
Challenges we ran into
Syntax and Formatting were two of the biggest challenges we ran into. Having each selection input correspond to various arrays was probably the biggest challenge - for the longest time, we would constantly get an error message telling us that Calculate was the only selection allowed. This was more of a formatting syntax error where the code was disorganized, but we had to run in loops as we didn’t know how to fix the constant error message. In addition, we constantly had to fix indenting errors where the code wouldn’t compile because one line had an error that led to the next.
Accomplishments that we're proud of
Although UniFinder is just a beginner platform, we are proud of the amount of functioning code we have been able to write in such little time. We started around mid-July, and our development process is nearly done. We are currently working on integrating the code (Python) into our website, which runs using JS, CSS, JavaScript, and HTML. We are also proud of our work distribution - as a team of two, we both worked outside our comfort zones to make sure the tougher portions of the project were taken care of.
What we learned
We learned a lot from the little time we have worked on this project. We learned that it is difficult to generate functioning algorithms that result in full accuracy, especially in the case of such a large data-range. We learned how to deal with different types of syntax and formatting errors as Python is not either of our most comfortable programming languages. But most importantly, we learned how to divide and distribute the work amongst ourselves while simultaneously learning more about the language as a whole - it taught us more about teamwork and the programming language, which is valuable for future use.
Log in or sign up for Devpost to join the conversation.