Scientist meets web dev: how Python became the language of data
Python started as a scripting language, but now it is the new trend everywhere and in particular for data science, the latest rage of computing. It didn't get there by chance: tools and concepts built by nerdy scientists and geek sysadmins provide foundations for what is said to be the sexiest job: data scientist.
In my talk I'll give a personal perspective, historical and technical, on the progress of the scientific Python ecosystem, from numerical physics to data mining. What made Python suitable for science; How could scipy grow to challenge commercial giants such as Matlab; Why the cultural gap between scientific Python and the broader Python community turned out to be a gold mine; How scikit-learn was born, what technical decisions enabled it to grow; And last but not least, how we are addressing a wider and wider public, lowering the bar and empowering people.
The talk will discuss low-level technical aspects, such as how the Python world makes it easy to move large chunks of number across code. It will touch upon current exciting developments in scikit-learn and joblib. But it will also talk about softer topics, such as project dynamics or documentation, as software's success is determined by people.
Computer science researcher
Gaël Varoquaux is an INRIA faculty researcher working on data science for brain imaging in the Neurospin brain research institute (Paris, France). His research focuses on modeling and mining brain activity in relation to cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.