Academic Weapons

Inspiration

As complete beginners, our team came to learn how to approach data science both practically with code, and conceptually with exploratory approaches.

What it does

We were able to filter large data sets for subsets of interest.

How we built it

We used Python in both Spyder and Google Colab to explore data with the help of mentors and each other.

Challenges we ran into

As newcomers to Python, learning the abilities of the language and how to implement them with correct syntax was our biggest challenge.

Accomplishments that we're proud of

We were very excited to experience several "aha" moments when we understood how to successfully define functions and apply filters to yield specific data.

What we learned

We all gained familiarity with Python language and a greater insight into how to approach large datasets. We also experienced the challenge of ambiguous data and how to qualify our interpretations with assumptions in order to maintain integrity within our project.

What's next for Academic Weapons

Concerning this project, the maternal education data presents an interesting avenue of investigation with important real-world effects. Maternal education could be correlated with maternal age at the time of childbirth, showing (or approximating) teen pregnancy rates. Plotting those variables with respect to geography would show areas in need of social programs to support teen moms and their children, as well as providing education and other resources to reduce teen pregnancy rates and consequently improve school retention rates.

Concerning personal development, all of our team are interested in improving our data science skills by continuing to learn programming languages and practicing approaching problems that initially seem beyond our skillset.