PyDev of the Week: Sophia Yang

This week we welcome Sophia Yang (@sophiamyang) as our PyDev of the Week! Sophia is a data scientist who works at Anaconda. You can connect with Sophia on LinkedIn. If podcasts are your thing, you can catch Sophia on this Teaching Python Podcast from last year.

Let’s take a few moments to get to know Sophia better!

Can you tell us a little about yourself (hobbies, education, etc):

I’m a Senior Data Scientist and a Developer Advocate at Anaconda. I’m passionate about the data science community and the Python open-source community. I made several Python open-source libraries such as condastats, cranlogs, PyPowerUp, intake-stripe, and intake-salesforce, and I serve on the Steering Committee and the Code of Conduct Committee of the Python open-source visualization system HoloViz. I hold an M.S. in Computer Science, an M.S. in Statistics, and a Ph.D. in Educational Psychology from The University of Texas at Austin.

Why did you start using Python?

I started using Python in college when I took my stats classes : )

What other programming languages do you know and which is your favorite?

I learned Matlab, C++, Rust, Go, and Kotlin in classes and I have used R/Stata/SAS for research in grad school. Python is my favorite. I love Python’s rich ecosystem and open-source community.

What projects are you working on now?

This week we are working on collaborating with Hugging Face and getting HoloViz and Panel on Hugging Face. HoloViz is a set of open-source and interoperable tools for making sense of data at any scale, primarily through visualization in a web browser. I hope the Hugging Face community will like using HoloViz and Panel as much as I do.

Which Python libraries are your favorite (core or 3rd party)?

hvPlot and Panel are my favorites. hvPlot allows me to build interactive plots super easily. It’s built on Bokeh, Matplotlib, and Plotly. I use Panel to build interactive apps and dashboards.

How did you decide to be a data scientist?

During graduate school, my research was all around understanding data and building models and I wanted to keep doing that after I graduate.

Do you really need lots of math to be a data scientist? Why or why not?

Yes and no. Everything is learnable. You can learn a lot of things on the job. A good math foundation might help you learn things faster. There is a lot more than math in a data science job. Programming skills, understanding business logic, and communicating skills are also essential for success in a data science role.

Is there anything else you’d like to say?

I have a Data Science and Machine Learning book club. We read one book a month and chat with the book authors by the end of the month. Check out my book club here: http://dsbookclub.github.io/.

Thanks for doing the interview, Sophia!