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 365体育投注, 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 365体育投注such a resounding success.