jeroenjanssens

11 ms·

1. 61.▲
   by jeroenjanssens 12y ago

   Comes with a great command-line tool.
2. 62.▲
   by jeroenjanssens 12y ago

   Hence the interrobang in the title. I hope that someone smarter than me can alleviate those two issues.
3. 63.▲
   IBash Notebook(jeroenjanssens.com)

   14 points by jeroenjanssens 12y ago|2 comments
4. 64.▲
   Predicting at the command line [video](jeroenjanssens.com)

   2 points by jeroenjanssens 12y ago|0 comments
5. 65.▲
   Pipecut: Interactive Pipeline Environment(pipecut.org)

   2 points by jeroenjanssens 12y ago|0 comments
6. 66.▲
   by jeroenjanssens 12y ago

   Author of Data Science at the Command Line here. Thanks for the nice blog post and for mentioning my book here. While we're talking about the subject of education, allow me to shamelessly promote a two-day workshop that I'll be gi
7. 67.▲
   Thank HN: Data Science at the Command Line Just Got Real(datascienceatthecommandline.com)

   2 points by jeroenjanssens 12y ago|0 comments
8. 68.▲
   by jeroenjanssens 12y ago

   Related: http://datascienceatthecommandline.com/#tools
9. 69.▲
   by jeroenjanssens 12y ago

   As a complement to this nice tool, I can recommend sshfs [1], which enables you to mount a remote machine as a local folder. [1] https://www.digitalocean.com/community/tutorials/how-to-use-...
10. 70.▲
    by jeroenjanssens 12y ago

    First blog post was the inspiration for a book, which is almost finished: http://datascienceatthecommandline.com
11. 71.▲
    by jeroenjanssens 12y ago

    Try `body`: https://github.com/jeroenjanssens/data-science-at-the-comman... $ echo 'header\ne\nd\na\nb\nc\nb' | body sort | body uniq header a b c d e
12. 72.▲
    Data Science at the Command Line [book] [webcast](datascienceatthecommandline.com)

    4 points by jeroenjanssens 12y ago|0 comments
13. 73.▲
    by jeroenjanssens 12y ago

    I have played a bit with XMLStarlet [1] and xml-coreutils [2], but not too much. Let me know how you find them! [1] http://xmlstar.sourceforge.net [2] http://xml-coreutils.sourceforge.net
14. 74.▲
    Ask HN: What's your favorite command-line tool for working with data?

    8 points by jeroenjanssens 12y ago|16 comments
15. 75.▲
    by jeroenjanssens 12y ago

    I'd just use xml2json and then apply jq. Usually gets the job done. The benefit of that is that you only need to learn jq. See this blog post I wrote a while ago for an example: http://jeroenjanssens.com/2013/09&#x
16. 76.▲
    by jeroenjanssens 12y ago

    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 cont
17. 77.▲
    by jeroenjanssens 12y ago

    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&#x
18. 78.▲
    by jeroenjanssens 12y ago

    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 creat
19. 79.▲
    Obtaining, Scrubbing, and Exploring Data at the Command Line(hakkalabs.co)

    5 points by jeroenjanssens 13y ago|0 comments
20. 80.▲
    Full-fledged chat server in 5 lines of bash(gist.github.com)

    7 points by jeroenjanssens 13y ago|1 comments
21. 81.▲
    by jeroenjanssens 13y ago

    Thanks! I completely agree with you on making the analysis reproducible. IPython Notebook is a wonderful tool, and I use it a lot myself. (You can even run shell commands from IPython.) However, using the command-line doesn't necessari
22. 82.▲
    Lean, mean data science machine(jeroenjanssens.com)

    13 points by jeroenjanssens 13y ago|2 comments
23. 83.▲
    by jeroenjanssens 13y ago

    See https://github.com/jeroenjanssens/data-science-toolbox/blob/... for a bash script that interfaces with explainshell.com. I wrote this script a while ago. I just needed to update the request because the AP
24. 84.▲
    by jeroenjanssens 13y ago

    Thanks! I hope you understand that I had to be brief in the blog post. Since you seem so interested, I recommend you read the technical report. Do let me know when you've got any questions after that. If you want, you could even clone
25. 85.▲
    by jeroenjanssens 13y ago

    The brief explanation of SOS (which starts at the top of the blog post) uses a toy dataset. This toy dataset has 6 data points. Each data point has two features. In other words, we have 6 data points in a 2-dimensional space. As for the rol
26. 86.▲
    by jeroenjanssens 13y ago

    Thanks :-) The algorithm is unsupervised, so it doesn't take any labels (i.e., party) into account. A senator is considered to be an anomaly when its 172 votes are too dissimilar from those of all the other senators.
27. 87.▲
    by jeroenjanssens 13y ago

    Thanks! For the graphics I used TikZ, which is a LaTeX package. The TikZ code of all the figures I used in my Ph.D. thesis are available on Github: https://github.com/jeroenjanssens/phd-thesis/tree/master&#x2F
28. 88.▲
    by jeroenjanssens 13y ago

    A brief explanation of the algorithm can be found at the top of the page: http://jeroenjanssens.com/2013/11/24/stochastic-outlier-sele... ;)
29. 89.▲
    by jeroenjanssens 13y ago

    Impressive. I'll add it to the list.
30. 90.▲
    by jeroenjanssens 13y ago

    Nice one!