As we wait for the SciPy talk videos to make their way onto the web, we’d like to share a short film recapping SciPy 2012.
The latest iteration of the SciPy conference was another great example of the scientific python community coming together to share “the latest and greatest.” Most organizations want to change the world in some way or another. At Enthought, we attempt to do this by building tools that help our customers – in both academia and industry – concentrate on solving their actual problems rather than wrestling with technology. We believe Python’s ability to operate smoothly in different contexts (e.g., desktop, web, array-based and distributed computing, etc.) makes it a highly productive and pragmatic tool with which to build solutions.
The SciPy community is changing the world by continually pushing technical computing forward in a pragmatic way. One just has to look at the content and tools presented at SciPy historically to know that this community has been been up to its neck in “data science” for some time. One could also argue, however, that SciPy is one of the best kept secrets in technical computing. As the recent focus on MapReduce solutions illustrates, the world is in the grips of “big computation.” It will only get tougher in the foreseeable future. At the same time, “big data” is a relative term. “Big” for a bioinformatician is different than for a macro hedge fund analyst, and these differences can often be measured in orders of magnitude. And when it comes to solutions, rarely does one size fit all.
In contrast, SciPy addresses a broad array of problems. SciPy 2012 offered High Performance Computing and Visualization tracks, with tutorials on machine learning, plotting, parallel computing, and time series analysis. Sometimes all these topics could be found in a single talk (see VisIt). The community also demonstrated some open-mindedness by inviting Jeff Bezanson, one of the authors of Julia, to share his experience building a language specifically designed for technical computing. It turns out there is a fair amount of overlap between what the SciPy community and the Julia team are planning. With LLVM IR increasingly being viewed as a common target, there is real excitement about what the future holds for language development and interaction.
This is all to say that SciPy has a lot to offer the world. Stay tuned for a bigger and better SciPy next year!