Toryn, a 17-year-old software engineer with a wealth of programming experience, set out to explore new horizons through Delta Careers' AWS Work Experience Program. Expecting a typical high school camp, he was instead met with a rigorous, real-world experience that challenged him to think like a professional in the tech industry. From collaborating on complex projects to connecting with industry leaders, Toryn's journey is a testament to how this program can transform a student's understanding of what it means to work in technology. Read on to discover how Toryn's passion for machine learning and software development was fueled by this unique opportunity.
Discovering the AWS Work Experience Program
When I first heard of the “AWS Work Experience Program,” I thought it was merely another high school camp that would teach me the very basics of some arbitrary programming subject—as do most others like it. However, I was pleasantly surprised by the expertise, rigor, and collaborative environment that the experience program implemented so well. I swiftly came to realize that this was not like other programs and truly taught students to behave as if they were developing a real product in the industry. From product management to complex computer development, Delta Careers’ AWS program provided me with fantastic learnings and practices that will assuredly aid me as I pursue my career path.
Diving Into the World of Technology
However, before I jump into specifics about my experience, I would like to share a little about myself. I’m Toryn, a 17-year-old software engineer who has lots of preexisting programming and industry experience, particularly in startup environments. I’ve built several projects involving frontend website development, backend server development, and machine learning (ML), meaning AI. I wanted to join the AWS Work Experience Program since I’ve been meaning to learn AWS tools for collaborative work and desired to sharpen up my machine learning skills in a professional program while also having fun; there are entire sides of ML that I have yet to explore.
From Theory to Practice: A Real-World Learning Experience
From this, you may believe that I am not the proper fit for such a program; with previous experience that revolves around the same subjects, how could I possibly benefit from participating in this AWS Work Experience Program? Turns out, a lot, but what’s more important is that my aforementioned experience allowed me to evaluate the program’s accordance with real-world programming experience. I found that the rigor surrounding scheduling and ensuring work gets completed was exactly like what companies would expect from a junior developer, and the consistent level of effort that the course required students to produce made me could collaborate effectively and create genuinely cool stuff in a time crunch. I learned many new things that I had not even known were missing from my knowledge, including terminology and programming strategies used for machine learning, and created an impactful project that everyone in my team of five proudly put on our resumes. In essence, despite my previous experience, there were still several things that made the program a super fun learning experience that ended in the creation of an impressive student-developed project that will be highly beneficial for my future career.
Building Connections and Learning from the Best
In addition to the content I learned, the AWS program was also fantastic for forming new relationships between people, whether it was those in my team or the mentors who were and remain always willing to help. It was refreshing to obtain advice from professionals in the industry who didn’t merely seem to be there because they were getting paid, but instead truthfully cared about each student’s project and wanted them to succeed in their career path. Not to mention that the mentors were incredibly good at teaching and communicating their ideas, developing our project and ensuring that our team was following best practices, analyzing our data correctly, etc. It was an overall extremely pleasant and insightful process. The mentors were also very knowledgeable about the subject being taught, which you may think would be inherent, but you would be surprised by the number of programs out there with mentors who know nothing about what’s being taught and are superficial in their help.
Overcoming Challenges and Collaborating as a Team
The AWS program, however, did not come without challenges. I’m not talking about mentors or the people I worked with—they were all great; rather, there were a few key blockers in terms of programming, machine learning model development, and communication that were key turning points once solved, reflecting a portion of the main learnings I obtained from the program. First, the tabular dataset our team chose had no explanation of its columns, and thus our team couldn’t determine the meaning behind much of the data we had chosen. Regardless, we were able to employ a few strategies we researched utilizing search strategies provided by our mentor to determine the significance of each data point and therefore whether it was okay to remove, leading me to our next big challenge, missing data. Our dataset also had many partial portions, requiring us to employ certain strategies to figure out what parts of it could be safely removed without losing anything significant. This was both a communication and strategic challenge because the dataset couldn’t be concomitantly cleaned by multiple people, and our AI model’s final accuracy was dependent on our ability to properly clean the dataset.
Applying New Knowledge in Machine Learning
In the end, our team devised a process in which everyone could contribute; we utilized AWS’s JupyterLab platform to collaborate in real-time while also employing Python graphing libraries to determine correlations between individual data points and the thing our model was trying to predict. This process worked fantastically and allowed us to learn more about the tools AWS offers—such as SageMaker Studio—how to configure them, and best practices for dataset analysis. I already had decent experience with the latter, but nonetheless learned something new while also doing it in a collaborative manner, which is a practice that will continue to follow me into my later professional life.
Reflecting on a Transformative Experience
Even if you weren’t able to fully understand everything I just discussed—maybe you don’t even know what Amazon Web Services or AWS is—that’s fine. What’s important is to let you know that the Delta Careers program is something I believe in and think is a fantastic way of kickstarting or continuing one’s professional high school career. As a huge advocate for high schoolers’ abilities to contribute to professional environments, my experience with the program taught me that this is a genuine way of ensuring students have those professional work fundamentals down while still having fun. After this and continuous programming practice, I would recommend all students begin seeking real work experience because the skills, connections, and mentoring you gain from this program are just that good.
Final Thoughts
The application may take a bit and is competitive, I know, but the experience is worth it based on what you can learn and who you can connect with. If you’re passionate about learning data science, machine learning, and making an impact—or even just one of these things—go fill out that application! As Elon Musk said, "When something is important enough, you do it even if the odds are not in your favor." The AWS Experience Program is that important opportunity to advance your career and make a difference.
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