PyData Triangle January 2020 Meetup

PyData Triangle starts a new year and a bi-monthly format on January 15 with a buffet dinner at 6:00, some mingling among our members, interesting presentations, and high energy discussions. We look forward to seeing you there.

Speakers:
• Armin Rosic: Computational Graphs
• Schaun Wheeler: Data science career advice to my younger self

Lightning Talks:
• YOU! Sign-up for a 5 minute lightning talk slot at the meeting. Or pre-sign-up by sending a message to the organizers.

Schedule:
• 6:00 - 6:30: Mingle and Food - Thank you Valassis for the great dinner
• 6:30 - 7:45: Presentations
• 7:45 - 8:00: Lightning Talks

The PyData code of conduct ( http://pydata.org/code-of-conduct.html ) is enforced at this Meetup. Attendees violating these rules may be asked to leave the meetup without a refund at the sole discretion of the meetup organizer.

Propose a talk or tutorial for the meetup. Contact any of the organizers, Alice Broadhead, Gene Ferruzza, or Chris Calloway through meetup messages.

Follow us on twitter at: https://twitter.com/pydatatriangle

Computational Graphs by Armin Rosic:

Armin will explore the general utility of data-traversable graphs with computing elements as nodes. Armin graduated from Utica College in 2013 having studied mathematics, physics, and chemistry. After some exposure to computational work in biochemistry and number theory through his professors, Armin realized reasoning about data and computation underpinned most of his interests and he has been studying the computer science landscape ever since.

Armin is interested in a wide range of topics in theoretical and applied mathematics and computer science. When he is not studying, you might find him in the woods foraging for wild mushrooms or making improvisational music.

Data science career advice to my younger self by Schaun Wheeler:

Schaun is the data science lead for the Consumer Graph team at Valassis, where he mostly tries to design solutions to problems that have no right answers. He wasn't ever formally trained to be a data scientist, but he has worked as one in five different organizations, each in a different industry, over the past 10 years. He can look back over his career and see a lot of things he did right and even more things he did wrong. He has lots of ideas about what he could have done differently.