As a Python newbie, Anaconda helped out a lot. Getting SciPy/NumPy/Pandas etc working on a regular Windows machine was a huge pain compared to just installing a single application.
Continuum are great benefactors in the Python community. With the explosion of "data science" in the last few years, I'm very hopeful that Python will keep holding its ground against the plethora of domain-specific languages that pop up, and Continuum are a big part of that.
With Anaconda, I just tell my students to download a quick installer, slap on iPython or PyCharm, and it's ready to go. It's one less thing to worry about! The installation is dead-simple, and is almost exactly the same whether on Mac, Windows, or Linux. When I do data science, I don't want to have to be doing IT, too!
Works great if you have the time in the classroom. Sysadmin skills aren't among any of my learning outcomes, so I don't have time to cover it. My students are still trying to grapple with the idea of a for loop!
Fixxer, I totally agree that it's great that one be capable of doing these things, but sometimes it's not as important as other things that could be taught. Like acbart, sometimes I want to teach why/when to use a statistical algorithm and not teach them how to grab all the dependencies, troubleshoot whether they have gfortran installed, etc. This problem is horribly compounded teaching undergrads when you have Windows, Linux, and Mac users in your class, where the procedures for getting a working scientific stack vary, and the errors are often not the same across platforms.
When I install the scientific python stack on a new machine, I almost always just use Anaconda. I already know how to install the stack (I'll always value the weekend I spent in undergrad fighting with a customized BLAS in R!), but sometimes I have more pressing/fun things to do.
We would be absolutely stuck w/o conda's environment manager -- I can make sure we use exactly the same versions of the packages across our whole team.
Same. While I have a different, more custom install (homebrew), there is nothing simpler for students (or other people learning numeric/scientific/data-Python) than installing Anaconda.
(Plus seaborn - the only thing I recommend starting with, which is not (yet?) in Anaconda.)
Sure it does. I just wanted to say that the default set of packages is extremely well-chosen (as there is only one additional thing needed for my workflow when introducing Python).
We're able to more strategically invest in open source projects since we're not operating in hand-to-mouth mode.
We're able to pursue partnerships and push even better Python integration with industry partners, because we can now afford to make long-term investments like that.
We're able to hire more strategically to beef up the engineering team.
And we'll be able to field more sales and sales engineering personnel to ensure that customers doing cool things with Python/SciPy/PyData get all the support they need to succeed.
Hm, I was trying to contact them a while ago, as we seem like a good fit for each other. Never heard back, and I just accepted a offer from someone else.
Really sorry about that - we've been trying to clean up and streamline our hiring process, and your application must have gotten lost in the shuffle. I hope your new gig works out well!
Excellent news! The Anaconda distribution has been great for me, and CA's policy of free academic licensing for their go-fast extensions [0] is the cherry on top.
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[ 3.1 ms ] story [ 95.0 ms ] threadhttp://venturebeat.com/2015/07/22/python-distribution-vendor...
With Anaconda, I just tell my students to download a quick installer, slap on iPython or PyCharm, and it's ready to go. It's one less thing to worry about! The installation is dead-simple, and is almost exactly the same whether on Mac, Windows, or Linux. When I do data science, I don't want to have to be doing IT, too!
As a data scientist, admin/dev skills are a huge plus.
When I install the scientific python stack on a new machine, I almost always just use Anaconda. I already know how to install the stack (I'll always value the weekend I spent in undergrad fighting with a customized BLAS in R!), but sometimes I have more pressing/fun things to do.
(Plus seaborn - the only thing I recommend starting with, which is not (yet?) in Anaconda.)
We're able to more strategically invest in open source projects since we're not operating in hand-to-mouth mode.
We're able to pursue partnerships and push even better Python integration with industry partners, because we can now afford to make long-term investments like that.
We're able to hire more strategically to beef up the engineering team.
And we'll be able to field more sales and sales engineering personnel to ensure that customers doing cool things with Python/SciPy/PyData get all the support they need to succeed.
Like numpypy?
Oh well. Good luck with that pile of money, CA.
Like I always say: when these guys go public, I'll be the first in line to buy stock :)
Great team, great vision, great execution + no-bullshit, bottom-up approach.
[0] https://store.continuum.io/cshop/academicanaconda