From Brain Injury Biomechanics to Data Science: A Journey of Curiosity and Crunching Numbers
Imagine this — I was deeply immersed in brain injury research, running countless simulations and working with tons of data to understand how impacts affect the brain. My days were filled with intricate models and complex math. But one day, a new insight struck me.
As I sifted through all the data on accidents, sports impacts, and many other head injuries, I noticed something intriguing: We had an enormous amount of data, but it was often scattered and not fully utilised. I thought, “Why not use this wealth of data to gain a more comprehensive understanding of how injuries happen?”
Although machine learning and data science wasn’t part of my PhD work, I completed my research and began collaborating with like-minded individuals who were also exploring data science. As I started learning more about machine learning, I saw how these techniques could help make sense of the scattered data and uncover patterns we hadn’t previously noticed. It was like discovering a new tool that could piece together the puzzle in ways I hadn’t imagined.
The Great Transition
After completing my PhD, I decided to take some personal time off. It was a chance to recharge and reflect, and it led me to an unexpected path. During this break, I was involved in scientific writing to pay the bills. This experience not only helped me sustain myself but also kept me connected to the academic world. It was during this time that I fully immersed myself in data science. I began taking on projects and diving into the field full-time. The shift from brain simulations to data sets, and from research screens to data dashboards, was smoother than I expected. My background in complex systems and mathematical problem-solving proved to be a great foundation for data science.
A Key Resource
Among the numerous books I’ve read on data science, one that stands out for mastering the basic concepts of machine learning and deep learning is Data Science from Scratch by Joel Grus. I recently finished this book, and it has been incredibly helpful. In the upcoming blogs, I’ll delve into its insights and how it’s shaped my understanding of data science.
Freelancing Adventures
Now, as a freelance data scientist, I work on a variety of projects. One week, I might be scraping web data to analyse market trends; the next, I could be using event study toolboxes to examine the impact of news on stock prices. Each project presents a new challenge and an opportunity to apply my data skills in different ways. And honestly, I’m just loving it!
What else?
Outside of my data science work, I’m also learning Japanese at the N3 level. It’s been an exciting journey, full of moments where I felt like I was accidentally ordering a mountain of noodles instead of a single dish! My ultimate goal? To be writing blogs in Japanese within a year. Picture me one day, typing away in Japanese, while my Japanese data scientist friends cheer me on with virtual high-fives.
As they say in Japanese,
“七転び八起き” (Fall seven times, get up eight)
That’s the spirit I’m bringing to my language learning and data science journey.
What’s Next?
I’m excited to share my journey with you through this blog series. We’ll explore how data science can help us better understand the physical world. From cleaning messy data to building predictive models, we’ll see how data science connects with real-life problems. I promise it will be engaging and easy to follow. I hope you’ll join me on this journey!
