I don't get it. DST installs 3 tools: R, Python, and DST itself. R and its packages are VERY easy to install, Python and its packages are pretty easy to install, and you don't need DST until you choose to install DST. It's not like they are putting a large hard to configure stack (like Hadoop/Mahout/Pig/Hive/Spark ...) for you.

Author here. Although the software contained in the base is indeed relatively easy to install, you'd be surprised how often people struggle installing Python and its packages on Mac OS X or Microsoft Windows!

The initial reason I created DST was that I'm currently writing a book called Data Science at the Command Line. Installing the command-line tools discussed in this book is unfortunately not straightforward. I wanted to offer potential readers a Vagrant box (with a shell provisioner) that installs these command-line tools. Similar to what Matthew Russell has done for his book Mining the Social Web [1]. This blog post [2] provides some more explanation and compares four different solutions.

The software packages that you mention sure are exciting! If there is an author or teacher that wants to teach these to her readers or students, then we should create a bundle (i.e., Ansible playbook) for them!

[1] https://github.com/ptwobrussell/Mining-the-Social-Web-2nd-Ed... [2] http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-...

It's a pain to install BLAS and the numpy family on a box you don't have root on so I definitely see value in the VM approach. Does being in a VM degrade the performance to the point where this is mainly a teaching tool or is it comparable to a native install?

I half-expected a "toolbox" more like Orange.

(http://orange.biolab.si/)

Would love to see a few basic libraries install ahead of time (NumPy and SciPy, namely) and hear about performance implications of running virtually.

Cool tool though - I've done data science work for a few companies now and the most frustrating thing is always getting set up on their stack.

Appears to be unrelated from the Data Science Toolkit virtual machine image which frontpaged yesterday

https://news.ycombinator.com/item?id=7835097

That's a nice coincidence! (I can imagine that all these names can be confusing.) The Data Science Toolkit focuses on lots of interesting APIs. This blog post [1] provides a brief comparison.

[1] http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-...

I appreciate author's effort. But for any aspiring data scientists this website is much much more useful:

http://www.datasciencetoolkit.org/

Looks cool. When it reaches 1.0 you might want to include Beaker: http://BeakerNotebook.com

@ author, I had a trivial query , How is it different from Anaconda ?

Anaconda is an easy-to-install distribution that contains many many Python packages. It is not a virtual machine.

The Data Science Toolbox also contains Python and the most popular packages for doing data science. Besides that, it also contains R and, again, some great packages.

The main difference, though, is that the DST aims to be a platform where authors and teachers can create custom software and data bundles for their readers and students. (I have been thinking about creating a bundle that would install Anaconda on the DST.)

If you only need to use Python, then Anaconda may very well satisfy your needs. Good luck!