Big data have swept over the analytical science sector in MNC’s and other corporates. A giant responsibility for mathematicians and analytics with Big data appears to make sense of the quadrillion data dumps obtained, thankfully there are many software’s that help them in this task. Python and R are the frontline software’s that provide support to data analysis and statistical work. This article will bring limelight to the alternatives that are available for the same.
- SAS- SAS (Statistical Analysis System) is a software suite mainly functioning for advanced analytics, data management, business intelligence, multivariate analyses, and predictive analytics. SAS suite can retrieve data from a variety of sources and statistically analyse it. It can also mine and transform data from different sources. SAS software suite has more than 200 components along with quality control and econometrics. In 2002 SAS introduced text miner to hike its business intelligence and was well accepted.
- GNU Octave – As the part of the GNU Project, Octave is a high level programming language mainly designed for huge numerical calculations. Octave is highly suitable with MATLAB. It provides a command line interface to solve linear and non-linear equations in a numerical fashion and also provides room for other numerical experiments. It is also expandable with the help of dynamically loadable modules.
- Torch- Torch has freshly been in the public eye as it was used by Facebook and google for deep machine learning. It is an open source library with a scientific framework and a script language based on LUA that provides a wide range of algorithms and also gives an extremely fast pace with the help of the LuaJIT scripting language. What makes Torch distinguishable from other data mining software’s is that the core package contains a flexible N- dimensional array also known as a Tensor, which can support basic routines for multiple features.
- Rapid Miner– Rapid Miner formerly known as YALE is another data analytics software that can be used as an alternative to Python and R. The distinguishable factor of Rapid Miner is that it has a very visual interface with predefined process. Coding is completely avoided in Rapid Miner thereby being recommended to amateurs a lot. It’s mainly used in the field of health care. The major drawback of Rapid Miner is that it allows very little customization as compared to R.
- Julia- Julia is a relatively new analytical software with an immature ecosystem, but the promise shown by the beta models are great. Julia has a distinctive design type system with a fully dynamic programming language with multiple dispatch as its core programming paradigm, it also allows parametric types in the programming language. If you are developer more accustomed to Python there is a PYCall package that allows you to call python functions. It is specifically designed for parallelism and distributed computation. The other distinctive features of Julia are built in package manager, allowing you to manage multiple packages with ease.
All these machine learning software’s are challengers to R and Python, and the attained proficiency of R and Python will only help in utilizing these programmes. Good Luck.
About the Author: Vaishnavi Agrawal loves pursuing excellence through writing and have a passion for technology. She has successfully managed and run personal technology magazines and websites. She currently writes for intellipaat.com, a global training company that provides e-learning and professional certification training.
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