A Year of Learning and Growth: My Internship Wrap-Up

As my internship journey comes to a close, I’m taking a moment to reflect on what has truly been an enriching and transformative experience — one that began in Summer ’24 and continued through Spring ’25. I’m incredibly grateful for the opportunity to work in a collaborative, research-driven environment where I could contribute to meaningful, real-world projects while growing both technically and professionally.

This spring, I continued working on my research project the Forecasting Engine and took a deeper dive into time series analysis. Beyond just identifying seasonality, I explored more advanced characteristics like trend and concept drift. Using Python and the arch library, I conducted statistical tests such as KPSS (Kwiatkowski-Phillips-Schmidt-Shin) and Phillips–Perron to detect the presence and type of trends. It was incredibly rewarding to uncover hidden patterns in the data and gain a deeper understanding of the complexities behind accurate forecasting.

One of the most exciting parts of my internship was implementing Recurrent Neural Network (RNN) using PyTorch within a Dockerized environment. I was responsible for the full model development process: training, testing, tuning hyperparameters, and even creating a custom data preprocessing pipeline tailored to the model’s requirements. Once the model was ready, I helped move it to production and tested it with actual customer data. This hands-on experience gave me a clear understanding of how machine learning models are developed, deployed, and tested in real-world environments.

In addition to technical work, I also had the opportunity to participate in weekly research paper discussions where I reviewed and shared insights on topics like time series forecasting, machine learning, and large language models (LLMs). These discussions were incredibly valuable, allowing me to stay updated on emerging trends and exchange ideas with my colleagues.

As I wrap up this internship and approach graduation, I can confidently say this has been one of the most rewarding experiences of my career. The challenges I faced, and the knowledge I gained, have emphasized the importance of problem-solving, collaboration, and continuous learning. This experience has boosted my confidence in applying theoretical knowledge to practical problems and reinforced my passion for data science.

This internship was more than just a learning experience — it was a launchpad for growth. I’m thankful to Cynthia Freeman and Ian Beaver for their continuous guidance and support. The blend of technical challenges, research exposure, and real-world applications has given me confidence and clarity as I move forward in my journey in data science. I’m excited to keep learning, building, and contributing to impactful solutions.

#UTDMSITM #Internship2025