Which is the best language for data analysis ? When it comes to Data Science, a question always comes up R or Python for data analysis? Although there are many other possibilities, these two languages have polarized discussions about which tool to use for analysis. Both languages are simple (and free) to install and relatively easy to start using.
If you are starting your journey in the world of Data Science and have no experience with programming in general, it makes sense to learn R or Python first. Although there are other languages like SQL, Julia, Scala, Javascript etc, Python and R are much easier to learn.
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Python Vs R
R
R is a powerful language used widely for data analysis and statistical computing. It was developed in the early 90s. Since then, endless efforts have been made to improve R’s user interface.
Some of the benefits of R is as follows.
- The style of coding is quite easy.
- It’s open source. No need to pay any subscription charges.
- Availability of instant access to over 7000 packages customized for various computation tasks.
- The community support is overwhelming. There are numerous forums to help you out.
- Get high-performance computing experience.
- One of highly sought skill by analytics and data science companies.
R has a long and reliable history and a strong support community in the data science industry. Together, these factors mean you can count on online support from others if you need help or have questions about using the language. In addition, there are plenty of publicly released packages (over 5,000), which can be downloaded to extend the language’s capabilities. This makes R great for performing complex exploratory data analysis. R also integrates well with other programming languages like C ++, Java and C. When it is necessary to make heavy statistical or graphical analyzes, R shows its strength.
Python
Python is a general-purpose programming language that can do just about anything you need: data collection, data engineering, analysis, Web Scraping, building web applications and more. It is simpler to master than R if you have already learned an object-oriented programming language like Java or C ++. Furthermore, since Python is an object-oriented programming language, it is easier to write on a large scale and with robust code, than with R.
Although Python does not have as comprehensive a set of packages and libraries as those available for the R language, the combination of Python with tools such as Pandas, Numpy, Scipy, Scikit-learn and Seaborn, makes the language one of the best choices among Data science professionals. The language is also slowly becoming useful for tasks in Machine Learning and the basis for intermediate statistical work (previously only under the domain of R).
In general, you will not go wrong if you choose to learn Python or R for data analysis. Each language has its pros and cons in different scenarios and tasks. In addition, there are libraries for using Python with R and vice versa, so learning one will not stop you from learning and using the other. Perhaps the best solution is to use the guidelines above to decide which of the two languages to start learning and then strengthen your skill set by learning the other.
