PyData - Paris is proud to host its first meetup!
The first installment will be held at Université Paris VI (Pierre et Marie Curie) - we will provide more information about the meet up location to people who register for this event.
The meet up will feature the CEO of Continuum Analytics, Travis Oliphant, most notably as the primary developer of the NumPy package, and as a founding contributor of the SciPy package, as well as Gaël Varoquaux, an INRIA faculty researcher working on data science for brain imaging. Gael is a core developer of scikit-learn, joblib, Mayavi and nilearn, and a nominated member of the PSF.
7:00pm - 7:45pm Travis Oliphant. Building an Open Source Company
There are several business models that have been used to build a company around open source. Travis will provide an overview of these as well as describe the fundamentals behind both open-source as well as company building that lead to the both the opportunities and challenges of mixing open source with commercial activity. Views on what it means to be an "open-source company" versus a company that uses open source will also be discussed along with some of the opportunities that are currently available for today's entrepreneur. The long-term success of open-source relies on company participation and support which is why it is important to have many thriving companies whose business models rely on open-source success. Along the way, Travis will provide a high-level overview of the technology Continuum is creating that emphasizes the purpose of Anaconda and Continuum Analytics to empower people to solve the world's greatest challenges and sustainably grow open source ecosystem.
7:45pm - 8:30pm Gaël Varoquaux. Enabling open science and data science via software: scikit-learn
"Data science", with sophisticated data processing, is having a transformational impact on many facets of science and society. It is driven by a technological revolution based on statistical models, and software implementing them. Outreach, bridging the technical gap outside of the ivory tower of research lab and high-margin tech ventures, is
crucial to see data-science applications of the beaten track.
Scikit-learn is a machine-learning software that strives to reach many users and applications. Via the rich Python data ecosystem it can be embedded any domain or workflow. It has hundreds of thousands of users in a variety of field in the industry or in academia. I will discuss how we built scikit-learn to be easy-to-use and didactic; how we grew a community of open-source developers with a focus on collaboration; how we ensure quality in a statistical-learning codebase; how we try to distill the most important progress from the rapid pace of academic publishing; and how we are struggling to make the development sustainable.