After being in the data science field for some years now, I thought it would be good to take a moment to write about how I got here since, in my opinion, my journey has been somewhat atypical and it's always good to remember where you started.
My journey started in college where I first entered as an Electrical Engineering major but midway through my sophomore year I switched over to Ocean Engineering. This brought a great deal of surprise to my parents since I don't really much care for the beach, but in my defense, I do love the water.
What does an ocean engineer do?
One of the first questions I usually get when I tell people that I studied ocean engineering is, "What do ocean engineers do?". My rehearsed answer is that ocean engineering is a combination of many different engineering disciplines all applied in the marine environment. For example, I took classes from the mechanical, electrical, civil engineer and computer science departments. This suited my jack-of-all-trades personality quite well and since I didn't know what I wanted to do with my life, I saw it as an opportunity to sample different disciplines.
While this may not seem immediately relevant to data science, I learned a lot of useful skills which laid a solid foundation in problem solving, analytical thinking and data processing. All of which would be further refined in grad school.
While in undergrad, I took a courses in underwater acoustics and data analysis and I knew this is was what I wanted to pursue for grad school. With that, and the job market being pretty poor at the time, I couldn't think better way to wait it out for at least a couple of years. There was also the option of a Ph.D. if the outlook was still poor afterwards.
I was extremely fortunate to have had a graduate advisor that was in tune with the industry trends and he was able to secure a fully funded project for the U.S Navy for my thesis . The project evolved over time but eventually landed on feature engineering for underwater targets using sonar images.
To assist me in the research I took an introductory data mining course using Weka. This was my first exposure to machine learning and building predictive models. Long story short, I defended my thesis and graduated then took a job doing absolutely nothing with data science for a defense contractor.
While my first job out of school didn't have me dealing with data science topics directly, I did learn some things that would help me later. I learned how to be a go getter when looking for information, I learned about the business side of things and worked on my communication skills while working on proposals and dealing with vendors. All of which I call upon today when engineering products and presenting research findings and product performance to an audience.
My time at this company ran it's course as I was looking to do more in terms of practical data analysis. Unfortunately, I found myself at a new company with similar tasking (albeit with some data analysis) but fortunately I met a group of people who were interested in machine learning. In collaborating with these people, I would find myself learning a new programming language (python) and exploring the world of machine learning through Kaggle competitions.
The very first competition in which we participated was the Galaxy Zoo competition. While I was still relatively new to all of this, I did have one skill I learned from my grad school days: image processing. I had spent a good amount of time learning to segment sonar images and I was able to apply that to this competition . I don't recall how well we did but it didn't matter because I was hooked.
I spent a good deal of time over the next year or so attempting other competitions from predicting Titanic survival to seizure detection. All the while honing my skills in practical data analysis, feature engineering and machine learning. With those skills I was able to land a job in the cyber security field applying all that I had learned to the detection of internet bot activity.
I've spent the last few years doing research and building systems to detect and mitigate malicious internet bot activity and I've learned so much it's hard to compile. The practical experience of building machine learning systems has been invaluable. I even learned things that aren't directly data science but help me to build better systems. Things like reproducible code and continuous integration and deployment. Oh and my python has gotten way better.
I'm not sure what the future holds but I know that, for now, I will continue to be learning as I move along and all my past experience will help me do and be better. I think it's important to remember that all our experiences help shape us as individuals and help us build skills to tackle challenges we may come across; career or otherwise.
If you've made it to this point, thank you for joining me on this nostalgic stroll down my journey to data science. I hope you found this entertaining and please feel free to reach out to me with any questions or comments you may have.
One Last Note
I wouldn't have gotten this point so far in my career without the help of some pretty amazing people along the way. I feel it is important to remember that and remain humble and grateful no matter where life takes me. If you're one of those people and you're reading this, thank you.
|||Publications and Presentations|
|||Basic Image Segmentation Using Python and Scikit-Image|