Take a little time to experiment and find the one that fits best. If you don’t have time or need to learn an entire programming language, an online universe of open-source software can provide you with multiple solutions for your specific needs. Python is a general programming language strong in algorithm building for both number and text mining.īased on my own user experience and research, here is a high-level summary for the three: Octave is good for developing Machine Learning algorithms for numeric problems. As previously mentioned, R’s strength is in statistical analysis. My suggestion is to try all three, and see which offering’s toolbox solves your specific problems. It would definitely prove easier for someone who has worked with Matlab to pick up Octave, as Octave is often described as the open source “clone” for Matlab.
Octave has a number of industry and academic applications, and engineers and analysts often utilize Python for building software platforms. That being said, R is popular among statisticians thanks to its emphasis on statistical computing. Which Software Package to Choose?Ĭan any one of these packages do more than the other two? The answer is probably no the three functionalities have a lot in common. If you want to use MCMC Bayesian estimation, R boasts MCMCpack, Octave includes pmtk3, and Python has PyMC.Īll three options feature large and growing user communities (i.e., the R mailing list) that serve as vital hubs for sharing information and exchanging experiences. For example, both R and Octave have simple zscore functions for computing Z-Score for Python, the function can be defined in a very straightforward manner: R, Octave and Python are flexible and easy to use for vectorization and matrix operations they’re not just data-analysis packages, but also programming languages for creating one’s own functions or packages.įor analysts who lack the time to engage in extensive coding, these open-source packages also offer some very handy built-in functions and toolboxes. When it comes to machine learning (the creation of algorithms that allow machines to recognize and react to patterns), matrix decomposition algorithms are critical. In fact, many are turning to R, Octave and Python with exactly this goal in mind. However, analysts can still rely on open-source software and online-learning resources to bring data-mining capabilities into their organization.
Some businesses want all the benefits of a top-shelf data analysis package, but lack the budget to purchase one from SAS Institute, MathWorks, or another established, proprietary vendor.
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