New name for blog: Data Sorcery with Clojure

I Just renamed the blog; previously it was called “Data Analysis and Visualization with Clojure,” and now it is simply called “Data Sorcery with Clojure.” I’ve also changed the default URL from http://incanter-blog.org to http://data-sorcery.org (the former URL now redirects to the new one).

Why rename it? Because “data sorcery” fits the theme of the name Incanter, it captures the perception people have of both statistics and lisp languages as dark arts, and it broadens the scope from just data analysis and visualization to include all the other machinations necessary when making sense of data.

4 responses to “New name for blog: Data Sorcery with Clojure

  1. Catchy name!

    Btw, the menu sections to the right of this blog appears to contain duplicate entries for Clojure, Statistics, Visualization, Programming. Guess that’s not on purpose. :-)

  2. You have made very interesting site. I’m also interested in data processing. Currently I am in a (state) or process of choosing language for realisation my ideas in the neural network area. Mostly I’m interested in PNN аnd RBM types of ANN.
    Recently, I opened for myself FP paradigma, realised in Clojure. I’d like to know your opinion regarding better way for realising ANN project. Should I use FP or OO programming for my objectives. What are the pros and cons?
    I know only that FP (Clojure) let me easily implement concurrency.

    Thank you.

    • I suppose choosing between OO and FP for implementing neural networks, or any other programming task, is a matter of taste.

      I prefer FP for all of the programming tasks I do, but especially for analytic programming. Neural nets are just mathematical constructs, and so lend themselves particularly well to functional programming.

      Of course, there are several neural networking packages written in Java and Python, most if not all of which are object-oriented; so, it comes back to being a matter of taste.

      Although, as you mentioned Clojure is well suited to concurrent processing. This is a particularly useful feature for implementing neural-nets.

      Good luck,
      David

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