Bringing Astronomy Down to Earth: Alumni Spotlight on Tim Weinzirl

Tim was a Fellow in our Spring 2017 cohort who landed a job with one of our hiring partners, First Republic Bank.

Tell us about your background. How did it set you up to be a great Data Scientist?

My education includes a B.S. in Physics from Drake University and a Ph.D. in Astronomy from the University of Texas at Austin. After grad school, I went overseas for a Research Fellowship at the University of Nottingham. Astronomers do a lot of coding relative to other fields, and having been coding in Python since 2006 for work, I was very familiar with the Python SciPy stack. Since 2014, I have also been volunteering time to data science and software engineering projects for a people analytics startup. This was extremely useful because it provided references in industry who could vouch for my data science skills.

What do you think you got out of The Data Incubator?

I got several useful things out of The Data Incubator: Strategies for resume writing, experience building and deploying a live web application, and a comprehensive set of IPython notebooks that encapsulate the advanced features of scikit-learn, SQL, and big data tools (Hadoop, Spark).

What is your favorite thing you learned at The Data Incubator?

Parallel computing with Spark.

Could you tell us about your Data Incubator Capstone project?

My capstone is a recommendation system for learning new programming skills. It is an application of item-based collaborative filtering to data from StackOverflow.com.

How did you come up with the idea for the project?

My capstone was inspired by my previous involvement in people analytics. I was well aware from my industry contacts that picking new skills to learn is a major problem in career development. I also had prior experience with the StackOverflow API, which came in handy when evaluating the project’s potential.

What technologies did you use and what skills did you learn at TDI that you applied to the project?

TDI provided training on the Flask web framework and deploying projects to Heroku. I used both of these skills to develop the final product. My recommendation algorithm also benefited from the TDI module on recommendation systems.

What was your most surprising or interesting finding?

The most surprising finding was the discovery that the recommendation system could reproduce StackOverflow community answers to “What Should I Learn Next?” questions. For example, someone asked what else on top of html+css was needed to build a website for sharing images. The community recommended a server-side language (e.g., PHP, Asp.net), javascript, and a database (e.g., MySQL). Given this person’s StackOverflow profile, my recommendation system reproduced the three major recommendations from the community.