“The dose makes the poison.” This age-old adage reveals a universal truth: any substance can become toxic if its concentration surpasses a certain threshold. But in today’s world, where new chemicals are introduced into the environment at an unprecedented rate, how do we assess the risks they pose to our health and the ecosystem? The answer lies in data analytics.
Imagine discovering a new pollutant in your local water supply. The first question that comes to mind is: “Will it harm me?” This brings us back to the original statement—the dose makes the poison. However, the challenge isn’t just detecting the presence of the pollutant, but understanding how it behaves in different environments. At varying temperatures, chemicals can either interact with their surroundings or pass through them with minimal impact. The ability to quantify these behaviors is where the predictive power of data analytics becomes essential.
In my research, I used data analysis to tackle this complexity by studying how pollutants interact with environmental factors to predict their behavior and assess the potential risks they pose. Specifically, I focused on predicting the equilibrium partition coefficient—the ratio that describes how a chemical distributes itself between two phases. Imagine a mason jar filled with equal amounts of water and oil, two substances that don’t mix, forming distinct layers. If you introduce a pollutant into this environment, it can do one of three things: it could dissolve entirely in the water, entirely in the oil, or distribute itself between the two. The models I built aimed to predict this behavior, and they can be applied to many other similar systems.
Engaging in this research fundamentally changed my perspective. What began as an environmental chemistry project quickly evolved into a broader exploration of how data can solve complex problems. I learned to look beyond individual chemical interactions, gaining a deeper appreciation for the power of data science in uncovering patterns and relationships across various domains.
The hands-on experience with environmental data transformed my understanding. I didn’t just analyze numbers; I explored the deeper meaning behind each variable. How did these factors interact chemically? How did they influence the target outcome? These questions drove me to dig deeper—not just into the data, but into the science itself. This fusion of chemistry and data analysis felt like tracing the outline of a puzzle until the full picture emerged in my mind.
Through this process, I realized something even more significant: data is not limited to environmental chemistry. It has the potential to uncover truths across any field. As I worked on more projects, I found myself chasing new curiosities and seeking continuous growth. I knew that my passion wasn’t just for the subject matter but for the underlying story data could tell about anything.
This realization led me to pursue a formal education in data science, and I found the perfect place at the Institute for Advanced Analytics (IAA). At the IAA, I’ve developed the tools and skills to better understand how data can be applied to any subject—whether it’s predicting chemical risks or exploring entirely different domains. I’m excited to see how this knowledge will continue to shape my journey as I dive deeper into the vast possibilities of data science.
Columnist: Akshay Podagatlapalli