The past eight weeks have truly been so beneficial, both from a learning and experimental perspective. Working with the various other team members, learning from the mentors, and gaining help from the TA has helped not only to understand the nuances of this new topic, but also to encourage a curiosity to explore.
When I initially joined this internship, I began with a small background in statistics and a knowledge of Java and Python. Although I was initially worried about learning a new field in Data Science and discovering Machine Learning for the first time, I attempted to build confidence and trust that learning these tactics through this experience would be possible.
The first few weeks, after exploring the data and visualizing the graphs, I was already enamored by the variety of possibilities that could arise. We were encouraged to ask our own unique questions of the data. Although the lectures and mini-courses were mostly taken on our own, we were able to come together in a collaborative environment to discuss our understandings, ask our compelling questions, and create new perspectives from the courses.
Once the initial exploration phase was completed, our tasks became unique. We met weekly with a common goal; however, we were able to put a unique perspective on our task, since each student’s respective questions were different. We were encouraged to learn different machine learning models and were constantly reflecting on our work and enhancing it with accuracy metrics. We were presented with different real life experiences from field scientists, professors, and undergrad students, giving us perspectives of Data Science in every facet of the career path.
Exploring with gender analysis was a rare experience. It allowed me to relate the real world gender disparities to a gender analysis in written expression. Writing the final paper also gave me the opportunity to research other gender studies and work on relating findings to various other field research.
The final presentation was not only engaging to create, but was also truly fun when working with our fellow peers. We had lively conversations about our project’s similarities and were interested to hear each other’s viewpoints to help each other.
Ending the course felt surreal. It was unrealistic to imagine that we had learned data science through virtual courses, explored machine learning algorithms, built models, graphed our data, tweaked our results, and collaborated on a final project all in what felt like a few short weeks. This experience truly helped me gain insight into an education and career path that was interesting to explore. I found myself gaining quick-learning skills with the courses, teamwork abilities with the other DIYAs, presentation skills with our weekly reports, and a connection, both with each individual mentor, TA, and DIYA there, and also with the field of data science itself.