R OR Python? Which Programming Language Is Best For Data Science
As the sources and amount of data gathered is increasing, organisations are looking for ways to gain actionable insights from it. Big data is capable of transforming various industries. But the challenge lies within the industries to make the right use of this data. Organisations rely upon data scientists for this. The number of big data experts in the market is not able to meet the rising demand for data analysis. This clearly states that data science is the safest option for any person looking for an in-demand and high paying career.
There are a number of things a data scientist can do as part of his role in a business. Starting with empowering the management to make better decisions through data driven evidence, they direct actions in a way to attain the set targets and identify opportunities in the market. In addition, they help in recruiting the right personnel by going through the vast amount of data available on social media and corporate databases to find the best fit for the organisation. With such a wide range of responsibilities there are some skills that one needs to attain in order to be called a data scientist.
Programming skills followed by the knowledge of statistics, machine learning, data wrangling, data visualization and communication, software engineering and data intuition are said to be some of the mandatory skills required for data science. To start with, programming skills are one the most important skills any employer looks for in a data specialist. There are a number of statistical tools out in the market but it is not possible for a person to know all of them.
Data Science in R or Python? Which Tool Is Worth Investing Your Time In?
There have been debates on which language is pertinent is data science. Some Data Scientists says R language can offer better functionalities and some say Python can resolve more problems, however both the programming languages have their specialized key features complementing each other. To come to a conclusion, we should first understand the pros and cons each of these programming languages. If you are unsure on which programming language to learn first then you are on the right page.
Data Science With R language
In Data Science with R, the programming language of statisticians and is also called as the “golden child” of data science. R’s commercial applications are ever growing and many MNC’s prefer data analysts and data scientists skilled in R.
- It is open-source and is freely available cutting down installation and upgrading costs for companies.
- The language is platform independent so can be used on any operating system.
- It also allows one to attribute names to objects and columns and rows of the objects making referencing easier.
- R permits integration with other languages such as java and C++ enabling interaction with many other databases.
R language also comes with certain disadvantages.
- Packages in native R tend to be slower than its competitors Python and Matlab.
- Being a flexible language, R makes it harder to maintain a proper coding standard.
- Some packages may become outdated due to goodwill and altruism of R users hence leading to R scripts not running with newer version of same packages.
Data Science With Python Language
Moving to Data Science with Python, it is a dynamic language that focuses on code readability.
- Its syntax helps in coding in fewer steps as compared to Java or C++.
- Python is able to integrate with Enterprise Applications that makes it easy to develop web services.
- It can process markup languages as it can run on all modern operating systems with the same byte code.
- The language has extensive support libraries that enables in increased productivity of the programmer.
The Python language also comes with certain limitations in terms of its usage.
- Python syntaxes are different from other programming languages which makes it hard for those who are accustomed to it to work on other languages.
- The language has also proved to be inadequate for mobile computing.
- It executes with the help of an interpreter instead of a compiler which makes it slow.
- It also requires more testing time.
- Python is also considered to be underdeveloped when it comes to accessing different databases.
Trade-Off Between R And Python:
Both the languages are free to download as compared to statistical softwares such as SAS and SPSS. Most of the developments in statistics first appear on these open source platform before the commercial platforms. These languages also have an added advantage of free support to users as opposed to paid support for the commercial platforms. A survey by O’Reilly states that data scientists that know primary open source tools earn a higher average salary.
So What Programming Language Should You Choose?
There are certain parameters to be considered before picking R or Python. Before going further with learning a language, the problem you are looking to solve and the field of application play key roles in choosing the kind of tool you should adopt. The amount of time available to invest in learning a new language is also important. Therefore, we can not say that one language is superior over the other. Both of them have their own advantages and drawbacks and can be implemented in different contexts.