Python or R? Which is easier to learn? Which one should I
learn first? These are some of the enquiries that often come from aspirants
looking to start a career in the data industry. This article will answer all
those questions and help you to have a good understanding of both the
Data Science is a flourishing industry, with a lot of organizations using data analysis to find ways to improve their business. The data industry will continue to be an attractive one for years to come and will create a lot of job opportunities for data professionals. According to a report by the American management consulting firm, McKinsey, there will be an “acute shortage of 190,000 professionals with deep analytical skills and 1.5 million managers with expertise in the industry” by 2018. This might not be a positive news for the industry but it is certainly a great news for data science professionals.
The demand will increase and the demand for experts in the field will shoot through the roof. If you’re interested in a data career, it is high time to get a certification to prove your skills. Enroll in a Hadoop course or become proficient in a programming language used for data analysis, like R or Python. However, it is not easy to learn everything at the same time. This is the same dilemma that data science aspirants face while making the decision of learning R or Python first.
Before we delve into the core matter of the topic, let us have a brief understanding of both the languages.
Also Read: languages to learn in 2017
Python – The Swiss Army Knife
Python is a high-level programming language used for general-purpose programming and is widely used across various industries. It can practically do a lot of things from data analysis to web application building.
Python is generally used by developers who want to delve into data science and feels comfortable due to its emphasis on productivity and code readability. It is simpler to use Python when you have a basic knowledge of object-oriented programming.
R – Focus on Data
R was primarily developed to provide a user-friendly way to do data analysis, statistics and graphical models. The usage of R was confined to academics and research at first. Now, with the corporate world discovering its benefits, it is one of the fastest growing statistical languages in the world. The biggest advantage of R is the CRAN, a huge repository of packages to which anybody can contribute.
The War of Preferences – R vs Python
There has always been a debate regarding the importance of one above the other when it comes to data science. There are various trends that show the comparison between the two in terms of popularity among the professionals and also in the industry. The idea of learning one before can give certain advantages to a candidate. E.g. getting a job opportunity.
However, one should not decide just by looking at the above data. Whether you go for a Python course or an R course first, varies a lot from person to person and the type of work associated with. A good knowledge of statistics can make it easier for you to learn R and put you in a position where you are very comfortable with the language. The same goes to Python, if you are experienced developer in object-oriented programming. In the end, it will depend on how you utilize the language to do data analysis efficiently.
Here are some of the guidelines that will help you in determining whether you should learn Python or R first:
Personal Preferences – As already mentioned, it depends a lot on personal preferences. Choose the one which will be easier for you to learn. Statisticians and mathematicians tend to prefer R, while software engineers or professionals from IT industry are more comfortable with Python. When you master one language, then it will be easier to focus on the other one.
Project Usage – What does the project require? If your data analysis task needs standalone computing on individual servers, then R is mainly used. Python is used when your task requires the need for web app integration or statistics code to be incorporated into the production database.
Regional Preference – During the initial years of the data science industry, the popularity of R was immense as its focus is primarily in that direction. Nowadays, the popularity of Python is gaining ground. Thus, you need to find out which is the popular one in your region.
Speed – When it comes to speed, R generally has a reputation for being slow as compared to Python. This is because the developers of R want the job done without putting effort into code optimization. However, R has a lot of packages and libraries which can help perform data analysis easily.
Visualizations – Data science often requires the plotting of data to showcase patterns. This is an area where R is ahead of Python as it provides easy visualization of data.
As you can see from the above points, both the languages have their own areas of advantage and it depends on your requirements or comfort of learning. Usually, if you want to join the technology industry as a data science professional, you should start with Python to put your career on a fast track. If you are looking to become a research or academic professional in the data science field or become a data science consultant to provide marketing or business analytics services, you should first learn R (as it focusses on data visualization and prototyping).
These tips are all to help you find out the best direction but in the end, it all depends upon you to choose your own path.