Data exploration of Pixar films: Which one is the best? w/ Eric Leung- nyhackr August Meetup

If you are attending the event in-person, please read the following:

• The event is located at New York University in Pless Hall - 82 Washington Square E, New York, NY 10003. It will be in Room 340 on the 3rd Floor.
• Doors open at 6:30 PM
• When arriving, please let the front desk know you are there for the meetup event on the 3rd floor.
• Pizza & Networking will start at 6:30 PM
• Talk will begin at 7:00 PM

If attending virtually, you will receive the Live Stream Link in your confirmation email. Event will go live at 7:00 PM (America/New_York).

Talk Title- Data exploration of Pixar films: Which one is the best?

Talk Description- Pixar makes beloved films, like Toy Story, Cars, and Inside Out. But which one is the best? Which franchise is the best? In this light-hearted but informative talk, Eric will go over a data R package he's created called {pixarfilms}. Eric will share some of his insights in answering some basic and not-so-basic questions he's had about Pixar films. For example, how have they done in the box office, and how they rank across critics on the internet and media. This latter ranking is done by consensus, where I'll aggregate rankings across different sources to come up with a data-backed, consensus ranking of Pixar films. The talk will also briefly highlight data challenges in curating the data and also highlight other data that can be found in the R package.

Bio- Eric is a senior data scientist and "small-batch data architect", where they have spent years measuring marketing effectiveness for iconic media brands including ESPN and ABC. Currently deepening their expertise in statistical programming, Eric is actively applying Bayesian methods and causal inference to both large-scale business problems and artisanal curation projects, such as structuring data for the Brooklyn Botanic Gardens and building a comprehensive Pixar film database. They are an active member in the New York data community and draws on a former work life as a computational biologist to contribute to open-source data and web development communities.