Monthly meetup

This session, run in collaboration with EdmontonPy & PyData, is perfect for data-oriented professionals, students, and enthusiasts to expand their skill set by learning more about Dask, an open-source library for parallel computing written in Python.

In this talk, you'll learn how to scale your PyData workloads with minimal code changes using Dask so that you can focus on your work without having to learn a new API.

While "Big Data" may be an overhyped buzzword, it's not uncommon for Python users to end up with more data than can fit on their laptops. Sampling is great, but sometimes you need to process everything. In the past, python users didn't have much choice beyond Spark (and the fact that most data lakes were HDFS made it the standard option). But today, even the stodgiest enterprises have migrated a ton of data to cheap blob storage in the cloud. This has freed python users from the misery of the JVM (I mean, hey, it's way better to see a Python error than a JVM stack trace, right?). So as a result, tools like Dask make it much easier to scale the tools Python users love, e.g., NumPy, pandas, sklearn.

Presenter: Gus Cavanaugh

Gus' Bio: I discovered Python sitting in a basement cubicle at a big consulting company. I fell in love when I was able to get stuff done and sort of understand it without having a degree in CS. As a consultant, I worked on data analytics and data science projects big and small. 4 years ago I left consulting to work at Anaconda, where I met wicked smart people like Matt Rocklin, the creator of Dask. Today, I work in sales for Matt's Dask company, Coiled. I post short videos about python + data science on LinkedIn. Please feel free to connect or shoot me an email (gus@coiled.io) anytime.

Connect with Gus on LinkedIn at https://www.linkedin.com/in/gustafrcavanaugh

A virtual meetup via Remo will be starting at 7 PM in collaboration with EdmontonPy & PyYYC. An online link will be provided before the event.