Academia-minded Christopher Pleasants fell hard for the science behind data and its inner workings. With a strong background in mathematics and quantitative research methods, the immersive data science track at General Assembly was a perfect fit for Pleasants when he sought out additional education.
After working on several projects that involved customer behavior and location-based modeling both in his past gig at AT&T and during General Assembly, Pleasants is looking to join an established team with ample mentoring opportunities. This data science enthusiast is full of good ideas — snatch him while you can!
What is your previous education?
I have a B.A. in American Studies, a Post-Baccalaureate Certificate in Math Education (which is all coursework required to complete a Bachelors in Math plus pedagogy courses), and an M.A. in Evaluation Studies (applied quantitative and qualitative research methods).
Why the interest in the technology field?
I was never really exposed to programming as a kid. However, when I discovered that there was a world of data science out there that I could access if I knew how to program, I decided to try to learn some Python. I picked it up really quickly, LOVED doing it, and knew it was the right thing for me!
What tech projects have you worked on?
For my capstone project at General Assembly, I used Natural Language Processing (specifically, a modified version of an unsupervised model called Latent Dirichlet Allocation) to identify sub-topics and sentiments in Yelp reviews to help users make better decisions more easily about where to eat. Someday, I want to refine the model and pitch it to Yelp.
Another side project I worked on is a Kaggle project that involves predicting the presence of West Nile Virus in mosquitos. I used a lot of techniques like feature extraction, threshold manipulation, regression, decision trees, Random Forest, gradient boosting, etc. What was most exciting was being able to think through exactly how my model could be used in a practical way. In my job at AT&T, I’ve also worked on several projects I’m really proud of that involved peer-group modeling and location-based modeling.
What tools are essential to you as a developer?
Can I just say, “Stack Overflow”? I really appreciate the abundance of information that’s available on the web and people’s willingness to share in the tech community. Seeing different people’s approaches and techniques has been very valuable for my development. Of course, R and Python are my most indispensable tools.
How do you stay informed and on-top of emerging trends?
I try to attend as many Data Science meetups in the area as I can. There are also several data science podcasts that I listen to and I keep up with some blogs. I frequently check the RStudio Blog, which outlines updates in the “Tidyverse” of R packages — it highlights the main changes so I can keep up-to-date on what new features I can take advantage of. Hadley Wickham is my hero!
What are your best technical or creative skills?
I would say my best skill is my ability to learn new skills. I pick things up very quickly and can usually get creative and adapt really fast.
What’s next on your list to learn?
I’m just started learning Scala, which is really exciting because it will help me a lot with many of the “Big Data” applications that run Java and Scala natively.
Are you interested in working for a startup, mid-sized company, or a corporate giant?
At this point, I think a mid-sized company is the right fit for me. I’m looking for a place that’s big enough to offer mentorship opportunities, but that is not so big that my thoughts and ideas get lost in the sea. But, if there are mentorship opportunities and my ideas get listened to, I’m open to any size company.