Join the Anaconda team at JupyterCon! We’ll have a booth with brand new demos, swag and more, plus catch awesome talks and tutorials from our team. Senior Solutions Architect James Bednar and Software Developer Philipp Rudiger will present their tutorial Deploying Interactive Jupyter Dashboards for Visualizing Hundreds of Millions of Datapoints and Software Engineer Steven Silvester, along with Jason Grout of Bloomberg, will present a tutorial on JupyterLab, both on Wednesday, August 23, from 1:30-5PM.
CTO and co-founder Peter Wang will present two talks at JupyterCON on Thursday, August 24—Jupyter & Anaconda: Shaking Up the Enterprise from 9:20-9:30AM, and Fueling Open Innovation in a Data-Centric World from 11:05-11:45AM. Data Scientist Christine Doig and Software Engineer Fabio Pliger will present their talk Leveraging Jupyter to build an Excel-Python Bridge on Thursday, August 24 from 11:05-11:45AM. Christine Doig will also present her talk ‘Data Science Encapsulation and Deployment with Anaconda Project & JupyterLab’ on Friday, August 25, from 11:55-12:35PM.
As a special treat, Leah Silen of NumFOCUS (@NumFOCUS) will also present her talk, Empower Scientists, Save Humanity, discussing the last five years of NumFOCUS’s mission, sponsoring almost twenty projects and becoming a recognized leader in diversity, open source code governance, and scientific software, and where the organization plans to go from here. Don’t miss it!
TUTORIAL: Deploying Interactive Jupyter Dashboards for Visualizing Hundreds of Millions of Datapoints, James Bednar (JamesABednar), Philipp Rudiger (@PhilippJFR)
The flexibility of Python and Jupyter notebooks makes it feasible to stitch together the various tools and libraries in the Python scientific software ecosystem to solve specific problems. However, it is often unclear how best to do so in each case, and a variety of technical problems typically arise in practice. Here we present an overall workflow for building interactive dashboards visualizing even billions of datapoints interactively in a Jupyter notebook, with graphical widgets allowing control over data selection, filtering, and display options, all using only a few dozen lines of code.
TUTORIAL: JupyterLab, Steven Silvester (@steve_silvester)
The tutorial will begin with a brief introduction and the motivation behind JupyterLab. We will then ensure that all users have JupyterLab properly installed on their personal machines. We will walk through the different features of JupyterLab in a live demonstration. Next, we will install and demonstrate a third party extension, a GeoJSON renderer. Finally, we will create our own custom extension that adds content to the application. We will then have time for questions and JupyterLab lab time for exploration and making custom extensions.
TALK: Jupyter & Anaconda: Shaking Up the Enterprise, Peter Wang (@pwang)
Open source has emerged as a valuable player in the enterprise in recent years. Companies like Jupyter and Anaconda are leading the way. Hear CTO and co-founder of Continuum Analytics Peter Wang discuss the co-evolution of these two major players in the new Open Data Science ecosystem and next steps to a sustainable future.
TALK: Fueling Open Innovation in a Data-Centric World, Peter Wang
Peter discusses why it is critical for us, as a community of socially minded technologists, to have a principled understanding of the core values that have manifested themselves thus far as open source. Peter then shares guidelines for how to carry those values forward, intentionally and thoughtfully, in a data-centric world where computational hardware and software development are increasingly commoditized.
TALK: Leveraging Jupyter to build an Excel-Python Bridge, Christine Doig (@ch_doig) & Fabio Pliger (@b_smoke)
Christine Doig and Fabio Pliger explain how they created a native Microsoft Excel plug-in that provides a point-and-click interface to Python functions, enabling Excel analysts to use machine learning models, advanced interactive visualizations, and distributed compute frameworks without needing to write any code. This was achieved using Jupyter kernels that run decorated Python functions in a notebook, written by any Python coder.
TALK: Data Science Encapsulation and Deployment with Anaconda Project & JupyterLab, Christine Doig
Anaconda Project abstraction combined with JupyterLab empowers data scientists to quickly iterate in the entire analytics process, reducing iteration time from data analysis, application development and deployment to production.