| Sumario: | JupyterCon New York 2017 was a powerful gathering of the data science and AI community that has gathered around Project Jupyter over the past 15 years. Its purpose: to share how the world's most data-driven organizations use Project Jupyter to analyze their data, share their insights, and create dynamic, reproducible data science. A sampling of the featured speakers at this inaugural conference included: Fernando Perez (Lawrence Berkeley National Laboratory); Lorena Barba (George Washington University); Demba Ba (Harvard University); Safia Abdalla (nteract); Brett Cannon (Microsoft); Jeremy Freeman (Chan Zuckerberg Initiative); Rachel Thomas (fast.ai); and Nadia Eghbal (GitHub). If you're looking for an understanding as to why Jupyter has become the new frontend for data science and AI, this video compilation of JupyterCon's live presentations will provide the insights you need. Highlights include: A front row view at each of JupyterCon's 55 sessions, 15 keynote addresses, and eight tutorials, including complete access to all of the conference's SOLD OUT talks, such as "Jupyter Widgets: Interactive controls for Jupyter" and "Deploying interactive Jupyter dashboards for visualizing hundreds of millions of data points in 30 lines of Python." Illuminating talks by 101 of the world's top Jupyter experts working at Harvard University, Continuum Analytics, UC Berkeley, DataScience.com, Bloomberg LLP, University of Pittsburgh, IBM, CUNY, Domino Data Lab, Cal Poly San Luis Obispo, Two Sigma, University of British Columbia, Civis Analytics, Columbia University, R-Brain, Lawrence Berkeley National Laboratory, Microsoft, and more. A thought provoking set of keynote addresses, such as Fernando Perez's (the progenitor of Jupyter) predictions for Project Jupyter's future; Wes McKinney's (Two Sigma Investment) vision for seamless computation and data sharing across languages; William Merchan (DataScience.com) outlining the three movements driving enterprise adoption of Jupyter; and Peter Wang (Continuum Analytics) describing the coevolution of Jupyter and Anaconda, two major players in the new open data science ecosystem. Four beginner-level Jupyter tutorials, including primers on using JupyterLab and creating JupyterLab extensions; using Jupyter widgets to build user interfaces; using Jupyter with visualization and analysis tools such as pandas, seaborn, Matplotlib, and scikit-learn; and an explanation of how Jupyter technology empowers research, engineeri...
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