Dream. Dare. Do – that is Suyati’s work principle in a nutshell.
In almost every region of data analytics and machine learning that is breeding innovation, we can see the rule of open-source tools. To cite a powerful instance, Python and R languages have set up a strong community of open-source libraries and tools for assisting data-scientists in carrying out analytical tasks.
Data analytics and machine learning have subtle differences. While machine learning gives an upper hand to accuracy of predictions, data analysis will focus more on interpretation of data and the statistical inference derived from it. Since, Python has geared its tools towards predictive accuracy, it has followers from the machine learning community while those who prefer R have gone for its statistical implications within data analytics. Nevertheless, both the languages can be used for machine learning and data analytics.
There are series of packages which allow replicating the functions of R in Python and vice versa. Packages within Python move towards strengthening the ability for statistical inferences, while R has libraries that aim to enhance the predictive accuracy.
While Python is known to have an inherent inclination for carrying out machine learning tasks, there are many features in it which toughens its machine-learning aptitude. Its library has a vibrant environment for testing out many machine learning algorithms, making it easy for you to compare their outcomes. Consider PyBrain- a modular machine learning library that has set of strong algorithms to carry out machine learning chores. These algorithms are dynamic and intuitive. Another candidate for help with machine learning is Scikit-learn which is developed on SciPy and NumPy. It is well-known for bringing in the capacities of data analysis and mining to advance the machine learning powers. SciPy and NumPy form the base for data analysis within Python. Anyone who takes data analysis seriously prefers to utilize them without any ornaments (that is, without the high-order packages).
While the buzz is that Python is driven towards assistance for machine learning, there are packages in its community which can boost data analysis as well. You can use data analysis gizmos and superior-quality structures included in prominent Python package Pandas. Check out RPy2 in case you want to perform advanced data analysis since it provides major functionalities of R language.
R is in the limelight for its data analysis capacities. Packages in the R libraries can allow you to transcend and augment such capacities. You can explore the packages available for three stages: pre-modeling, modeling and post-modeling along with those for particular tasks like continuous regression, model validation and data visualization.
When it comes to machine learning, R is still nurturing its roots. You can use Nnet for enhancing R and to model the neural networks. Another package which helps with the machine learning powers is Caret, which provides much functionality to improve the building of predictive models.
Here, let us discuss various criteria to help you decide which language would be optimal for you.
The first sub-section Consider using… is meant to give the traditional qualifications considered before using any language. Once you have gone through the preliminary list, the second section Contextualizing the Choice will let you finalize the language depending on the work you intend to do.
Consider using Python if you:
Contextualizing the Choice
R faces the issue of consistency with packages as many of the algorithms are offered by third-parties. This could delay the speed of development: you will need to learn new ways for modeling data and making predictions every time you use a new algorithm. The documentation in R language has also faced the charge of incompleteness and inconsistency. Regardless of these cons, R is the ideal choice for those who are undertaking research and academic tasks.
Python is definitely a better choice for those who are involved in professional tasks. Collaboration is easier with Python, apart from having chunks of R-similar packages and data analysis instruments.
Python and R have strong packages which can allow uniformity between them. Since there are multiple distributions, IDEs, modules and algorithms, it is possible to tackle almost every problem with them. However, in case you are insistent on a flexible, multi-project oriented and extendable language which can work with both data analysis and machine learning, then Python is the way to go.
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