Local Tech Groups Collaborate at Astraea, Inc.

Astraea Co-founder and CTO, Daniel Bailey, welcomes the crowd

The Astraea team hosted the June 2018 meeting of Charlottesville Data Science on June 28 at our offices in the Glass Building, just off the Charlottesville Downtown Mall. This month’s meeting was in collaboration with Python Charlottesville and featured talks by Phil Varner, an Astraea contractor, and Zach Anglin of S&P Global Market Intelligence.

Before the talks began, more than 50 community members came together to network, drink beer, eat pizza, and talk about all things data science and Python. Here at Astraea, we are dedicated to cultivating a vibrant and connected data science community in Charlottesville, and meetups like this move us toward that goal.

With over 500 members and attendance at meetups regularly nearing 100, the Charlottesville Data Science community plays a key role in making Charlottesville a hotbed of innovation. “It is amazing, the amount of data science talent in Charlottesville. With more than a dozen startups using Machine Learning and Artificial Intelligence to build products and industry leaders such as S&P Global and Merkle’s data science teams all based in Charlottesville, we have a unique environment that facilitates the kind of cross-pollination that you typically only find in bigger cities,” said C-Ville Data Science Meetup co-organizer and Astraea CTO Daniel Bailey.


Phil Varner speaking about Zappa and AWS Lambda

This month’s meeting was a double feature. Phil Varner, a software engineer at Astraea, kicked off the night with a presentation on “Functions as a Service with Zappa and AWS Lambda.” You can view his slides here.

Phil spoke about Zappa as a deployment system for event-driven, AWS Lambda-based applications. He also broke down the advantages and disadvantages of Lambda’s ‘serverless’ approach to providing functions as a service and covered some examples.

“Phil’s use of Zappa and AWS Lambda has been instrumental in our data acquisition pipeline at Astraea. His talk did a great job of demonstrating how a data scientist can rapidly set up a scalable deployment system without a lot of overhead,” said Bailey.

If you’re interested in Phil’s work and would like to connect with him, you can do so through his LinkedIn profile.


Zach Anglin, currently a Senior Data Scientist at S&P Global Market Intelligence and an expert on Python, presented next. He presented an “Introduction to Probabilistic Programming in Python.” Probabilistic programming, which has become an increasingly popular and highly debated topic in the data science world, uses a high-level language to enable programmers to re-frame probability models for easier computation. According to Anglin, “By automatically performing inferences, probabilistic programming greatly cuts down on the time required to solve models.” (You can find more information here.)

Anglin used the PyMC3 Package in Python to demonstrate a practical application of probabilistic programming. PyMC3 incorporates Bayesian statistical modeling and Probabilistic Machine Learning in a flexible way, allowing it to be applied across a wide variety of industries to solve a number of problems.

Anglin then made an interesting comparison of probabilistic programming versus traditional machine learning including an analogy to a Bop It. “His comparison led to a Q&A session and group debate that would have lasted into the wee hours of the morning if we didn’t have to kick everyone out. Even then a large group of us just moved the debate to the beer garden below our offices,” said Bailey.

If you would like to talk more to Zach about his talk, you can access his LinkedIn profile.


We hope that everyone enjoyed this month’s meeting. We’re excited to continue helping Charlottesville’s data science community grow. Be sure to join the Charlottesville Data Science Meetup page if you’re interested in attending meetings in the future. You can also follow Astraea on Twitter for company updates as well as reminders for events such as this one.

Written by Renee Spillane