Let’s take a few moments to get to know Nathan better!
Can you tell us a little about yourself (hobbies, education, etc):
I studied applied math at Columbia for both undergrad and grad school. In my free time, I listen to lectures and read a lot; particularly topics within the social sciences.
Other hobbies include music composition, blitz chess, hiking, and biking.
Why did you start using Python?
Scientific computing. The ease of use, readability, and library ecosystem makes it a great choice for so many tasks within that space.
What other programming languages do you know and which is your favorite?
Of those, I think C++ is my favorite. It offers a really nice balance of low-level control and ease of use. But my overall favorite is Python.
What projects are you working on now?
Outside of work, I’m collaborating with some friends on a computational biology paper and playing around with procedurally generated music.
Which Python libraries are your favorite (core or 3rd party)?
Some pretty typical stuff for data science and ML. Pandas, scikit-learn, NumPy, Keras, TensorFlow. For “big data”, Spark and MLlib.
I see you have done lots of talks. How did you get started doing that?
I got into giving talks through side projects. I figured if something is sufficiently interesting for me to spend time on it, it’s probably interesting to some other folks as well.
Do you have any advice for other people who would like to get into giving talks?
If you have ideas you’re interested in sharing, don’t hesitate to submit a proposal. A surprisingly large number of conferences are happy to work with first time speakers.
Is there anything else you’d like to say?
I’m a big believer in the importance of scientific computing. To that end, I’d love to recommend that people consider supporting some combination of the Python Software Foundation, the R Foundation, NumFOCUS, and Code Nation.
Thanks for doing the interview, Nathan!