AnacondaCON 2018 Recap: An Exploration of Modern Data Science

 

Last year’s inaugural AnacondaCON was a major milestone for our company. Our goal was to create a conference that highlights all the different ways people are using data science and predictive analytics, and reflects the passionate and eclectic nature of our growing Python community. When over 400 people descended upon Austin to connect with peers and share their latest projects and innovations, we knew we had achieved our goal.

Our second AnacondaCON, held in Austin last week, took that achievement to a whole new level.


AnacondaCON 2018 featured an amazing lineup of innovators and practitioners, on both technology and business fronts, who shared unique perspectives on the current state of data science. We hosted hundreds of attendees from all over the world—from Ecuador to Germany to Brazil to Norway to France—and of all ages, including a 15-year-old budding data scientist who gave up his weekend to attend a day of tutorials with his mother.

We opened the conference with a memorable keynote from Anaconda Co-Founder and CTO Peter Wang on the co-evolution of data science, data-driven business, and the open source Python community. And we closed out the conference with an inspiring keynote from David Yeager on the science of motivation and learning, and the practice of perseverance and tenacity.

In between, we mingled with fellow Pythonistas and learned from each other about how the latest data science tools are being applied in business, science, and research. We laughed together over the must-see trailer for Pyception, a deep learning box office thriller. We scrambled to find enough chairs to accommodate the eager crowd that swarmed into the machine learning with scikit-learn tutorial. We fostered life-long connections and created lasting memories at our offsite AnacondaCON Carne Offsite Party (and unofficial after-party!).


We also heard dozens of stories from the data science community’s most innovative and passionate thought leaders on how they’re using Python to shape technology and change the world. Wes McKinney shared the latest developments of the ongoing Apache Arrow project. Paige Bailey showed us how to use image recognition to take an existing deep learning model and adapt it to a specialized domain. Timothy Dobbins brought things to a personal level by demonstrating Achoo, which uses a Raspberry Pi to predict if Tim’s son will need his inhaler at school using weather, pollen, and air quality data.


We loved hearing thought leaders from our thriving data science community share their compelling projects, new analytic approaches, and best practices, and we can’t wait to do it again next year. On behalf of Team Anaconda, we want to thank all the attendees, speakers, and sponsors who came together to make AnacondaCON 2018 such a resounding success.


You May Also Like

Data Science Blog
Intake: Caching Data on First Read Makes Future Analysis Faster
By Mike McCarty Intake provides easy access data sources from remote/cloud storage. However, for large files, the cost of downloading files every time data is read can be extr...
Read More
Data Science Blog
Credit Modeling with Dask
I’ve been working with a large retail bank on their credit modeling system. We’re doing interesting work with Dask to manage complex computations (see task graph below...
Read More
Data Science Blog
Deep Learning with GPUs in Anaconda Enterprise
AI is a hot topic right now. While a lot of the conversation surrounding advanced AI techniques such as deep learning and machine learning can be chalked up to hype, the under...
Read More