Python or R

 

To be a entry level Data Analyst what skills you have or whether you say which Programming language you learn first Is it Python or R. So lets discuss this today.

Python

Python is more flexible. It is not as hard to learn, and it can be applied in most areas, apart from statistics and math. Are you more logical than mathematical? Do you think in terms of processes rather than formulas? Do you have a computer science background? Than Python is for you. With every inch of time, we move forward, we can observe that Artificial intelligence and Machine Learning are becoming the shine on the eyes of every developer. Which is why, beginner or expert, all are driving the “popularity traffic” towards Python. This is the reason for the increase in Python demand.

Claimed by some old school coders/working professionals, there are some negative conceptions concerning with the “non-serious” nature of Python. Yet still, increasing demand of python programmers seems to be at odds with this idea. For proving the same DataFlair have set down some key points.

R

On the other hand R is great for mathematical people. Think of R as spreadsheets on steroids. A lot of people progress from spreadsheets to R. These people are usually statisticians at heart. R is an increasingly popular programming language, particularly in the world of data analysis and data science. You may have even heard people say that it's easy to learn R!

Benefits of Python

Python holds a special place in the hearts of Data Scientists. Data Science is all about dealing with data at huge amounts (Big Data). Hence with simple usage and a large set of libraries and frameworks, Python has become the most promising option to handle it! e.g. PyBrain, PyMySQL, and NumPy are one of the big reasons. Another step forward is because of Python’s easy integration with other programming languages, making it more scalable and future-oriented. 

Benefits of learning R

• The R tidy verse ecosystem makes all sorts of everyday data science tasks very straightforward.
• Data visualization in R can be both simple and very powerful.
• R was built to perform statistical computing.
• The online R community is one of the friendliest and most inclusive of all programming communities.
• The R Studio integrated development environment (IDE) is a powerful tool for programming with R because all of your code, results, and visualizations are together in one place. With R Studio Cloud you can program in R using R Studio using your web browser.

Key Differences:

• R is mainly used for statistical analysis while Python provides a more general approach to data science
• The primary objective of R is Data analysis and Statistics whereas the primary objective of Python is Deployment and Production
• R users mainly consists of Scholars and R&D professionals while Python users are mostly Programmers and Developers
• R provides flexibility to use available libraries whereas Python provides flexibility to construct new models from scratch
• R is difficult to learn at the beginning while Python is Linear and smooth to learn
• R is integrated to Run locally while Python is well-integrated with apps
• Both R and Python can handle huge size of database
• R can be used on the R Studio IDE while Python can be used on Spyder and Ipython Notebook IDEs
• R consists various packages and libraries like tidyverse, ggplot2, caret, zoo whereas Python consists packages and libraries like pandas, scipy, scikit-learn, TensorFlow, caret

R vs Python - Job Trends

I've gathered 3321 job descriptions of positions that require working with data in Canada and the US, over the past year. I looked on LinkedIn.com, Workopolis.com and Indeed.com. From this little dataset, Python is shown to be almost 5 times as popular as R. Industries that hire people with Python, R or both, include: finance, consulting, telecommunications, and software.

Some of the job titles that require Python:

• Data architect
• Data analyst
• Data engineer
• Data scientist
• Developer
• Manager

Some of the job titles that require R:

• Data analyst
• Data scientist
• Investment analyst
• Manager
• Tax Staff
• Scientist

There is some overlap, and also some important differences. But you get the general idea.

Trying to be popular on the job market isn't easy. It is a constantly evolving environment, a little like the stock market. When something goes into high demand, it quickly becomes surplus.

Even if you are interested in Data Science course by Coursera Powered by Google. You can go through that and even I would love to say Google also Recommend R over Python. But take my advice first go through R and Master your skills in Python cause you able to solve more complex problems using Python compared to R.

In a nutshell, the statistical gap between R and Python are getting closer. Most of the job can be done by both languages. You’d better choose the one that suits your needs but also the tool your colleagues are using. It is better when all of you speak the same language. After you know your first programming language, learning the second one is simpler.

In the end, the choice between R or Python depends on:

• The objectives of your mission: Statistical analysis or deployment
• The amount of time you can invest
• Your company/industry most-used tool   

What's your Opinion Tell us in the comment section what u want to learn first.

𝕿𝖍𝖆𝖓𝖐𝖘 & 𝕽𝖊𝖌𝖆𝖗𝖉𝖘...

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About Freaky Analyst

A Passionate Data analyst working with large amounts of data and to turn this data into information, information into insight and insight into valuable decisions. I also have a keen interest in the field of data analysis, data visualization and am fascinated by the power to compress complex datasets into approachable and appealing graphics.