Data Scientist Spotlight: Ryan Hildebrandt

Ryan Hildebrandt is a psycholinguistics researcher with a background in speech recognition.
Written by
Datapane Team
Published on
17 January 2022

This week, we are speaking with Ryan Hildebrandt, who is a psycholinguistics researcher with a background in speech recognition, Japanese phonology, and emotion word processing. He also works as a Japanese language and culture instructor for Concordia Language Villages. In his spare time, Ryan enjoys working on linguistics projects in Python/R, baking, and drinking fine Chinese/Japanese teas.

Let’s get to know more about Ryan!


Q: How do you like to contribute to the data community?

Ryan: I enjoy making reports and applications that relate to my interests, as a way to teach myself different techniques and approaches in Python, R, natural language processing, and data visualization. If these exercises prove useful to others in the data community, then I’m happy with my contribution!

Q: Why do you prefer to visualize data using Python instead of a proprietary tool, like Tableau?

Ryan: I appreciate the flexibility of a tool like Python, in that it can handle every step from data collection and preparation through visualization and publishing. Not only the capacity to handle every step of a data project but the flexibility to handle projects in any number of disparate fields as well.

Q: What tips and resources would you have for someone looking to learn about Python data storytelling?

Ryan: Do projects and learn as you go! Trying to know every function in every package you could potentially need for a project will only bog you down, so pick something you’re interested in and start learning by doing.

Q: What do you think are the best ways to get noticed as someone telling stories with data?

Ryan: Make something that you’re personally invested in. There are plenty of examples of projects using the same datasets and the same techniques (nothing wrong with that! they can be very informative and worth doing!), but worth finding data that you care about and can dive into, both in that you will learn much more from these kinds of data and in that you will distinguish your work.

Q: What tips would you have for someone just starting out?

Ryan: Focus on getting a project (even a small one) or two done start to finish before diving into a bunch of disparate work. Nothing wrong with starting small!

Q: What are your favorite libraries and resources for creating visualizations and data stories?

Ryan: Some of my favorite libraries are Datapane (for publishing), Plotly, Bokeh, Seaborn (for plots and visualization), and R’s Shiny for more interactive standalone applications.

Check out Ryan's latest work