Most scientists go to conferences in their own field: neuroscientists go to the monstrous Society for Neuroscience (SfN); Bioinformaticians go to RECOMB, ISMB, or PSB; and so on.
People go to these to keep up with the latest advances in their field, and often, to do a bit of networking.
SciPy is a different kind of conference. It changes the way you do science. You learn about the latest free and open source software to help you with your work. You learn to write functions and interfaces instead of scripts, and to write tests so you don't break your code. You also learn to contribute these to bigger projects, maximising the reach and impact of your work (see "sprints", below).
And you learn these things by doing them, with the people who are the best at this, rather than by reading books and blog posts. (Which maybe I shouldn't knock, since I'm writing a book about all this and you are reading my blog!)
Attendees to SciPy have scientific software in common, but come from diverse fields, including physics, pure maths, data visualisation, geosciences, climatology, and yes, biology and bioinformatics. Mingling with such a diverse group is a fantastic way to get your creative juices flowing!
The conference lasts a full week and is broken up into three parts: tutorials, main conference, and sprints.
With a few exceptions, you won't learn about your own field. But you will learn an enormous amount about tools that will help you be a better scientist. If you are a newbie to Python, you can go to the beginner tutorials track and learn about the fantastic scientific libraries available in Python. If you already use NumPy, SciPy, pandas, and the IPython notebook, you can go to the intermediate or advanced tracks, and learn new things about those. Even as an advanced SciPy user I still get tons of value from the tutorials. (Last year Min RK gave a wild demo of IPython parallel's capabilities by crawling wikipedia remotely while building up a graph visualisation on his live notebook.) (Fast-forward to the 1h mark to see just the payoff.) Here's last year's tutorial schedule for an idea of what to expect.
the main conference track
You will also hear about the latest advances in the scientific libraries you know and use, and about libraries you didn't know about but will find useful (such as scikit-bio, yt or epipy). The main conference track features software advances written by presenters from many different fields. Hearing about these from the authors of the software lets you ask much different questions compared to hearing someone say, for example, "we used the Matlab image processing toolbox". If you ever had a feature request for your favourite library, or you wondered why they do something in a particular way, there's no better opportunity to get some closure.
The crosstalk between different fields is phenomenal. Hearing how a diverse set of people deal with their software problems really opens your mind to completely different approaches to what you had previously considered.
Finally, there's two days of coding sprints. Even if you are a complete beginner in software development, do yourself a favour and participate in one of these.
Two and a half years after my first SciPy in 2012, I'm writing a scientific Python book for O'Reilly, and I can 100% trace it to participating in the scikit-image sprint that year. With their guidance, I wrote my first ever GitHub pull request and my first ever unit test. Both were tiny and cute, and I would consider them trivial now, but that seed grew into massive improvements in my code-writing practice and many more contributions to open source projects.
And this is huge: now, instead of giving up when a software package doesn't do what I need it to do, I just look at the source code and figure out how I can add what I want. Someone else probably wants that functionality, and by putting it into a major software library instead of in my own code, I get it into the hands of many more users. It's a bit counterintuitive but there is nothing more gratifying than having some random person you've never met complain when you break something! This never happens when all your code is in your one little specialised repository containing functionality for your one paper.
The SciPy calls for tutorials, talks, posters, and its plotting contest are all out. There's specialised tracks and most of you reading this are probably interested in the computational biology and medicine track. It's taken me a while to write this post, so there's just one week left to submit something: the deadline is
April 1st Update: the deadline for talks and posters has been extended to April 10th!
Even if you don't get something in, I encourage you to participate. Everything I said above still applies if you're not presenting. You might have a bit more trouble convincing your funders to pay for your travels, but if that's the case I encourage you to apply for financial assistance from the conference.
I've written about SciPy's diversity problem before, so I'm happy to report that this year there's specific scholarships for women and minorities. (This may have been true last year, I forget.) And, awesomely, Matt Davis has offered to help first-time submitters with writing their proposals.
Update: A colleague pointed out that I should also mention the awesomeness of the conference venue, so here goes: Austin in July is awesome. If you love the heat like I do, well, it doesn't get any better. If you don't, don't worry: the AT&T Conference Center AC is on friggin overdrive the whole time. Plus, there's some nearby cold springs to swim in. The center itself is an excellent hotel and the conference organises massive discounts for attendees. There's a couple of great restaurants on-site; and the Mexican and Texas BBQ in the area are incredible — follow some Enthought and Continuum folks around to experience amazing food. Finally, Austin is a great city to bike in: last time I rented a road bike for the whole week from Mellow Johnny's, and enjoyed quite a few lunchtime and evening rides.