Are you enthusiastic about becoming a data analyst? If you wish to enter the data analysis industry, you need to be proficient with a programming language. Programmers often have to choose between Python and R.
But which programming language should you choose? Data analysts are required to learn various tools as well as core skills. If you check online sources you will find the essential data analyst skills required to be proficient with mathematics, statistics, Structured Query Language (SQL), programming, data visualization, etc.
Both languages have their use cases. Let’s look at the benefits and differences of both programming languages.
Differences between Python and R
Both the programming languages can run on any operating system, whether it is Windows, Linux, or even macOS.
Learning Python programming isn’t difficult for beginners due to the versatility of the language. Python is a highly interpreted programming language that is considered easy to learn due to its syntax. Python is used for various purposes such as data analysis, data science, and automating tasks that require manual effort. Python is used for coding various applications. It’s considered to be a general-purpose programming language. The approach of the language toward data science is generalist.
Moreover, Python has multiple libraries that can be quite difficult to comprehend for a user. The language can be used to build graphic user-interface and develop games as well. Other features of the Python programming language include that it is more useful for machine learning, deep learning, etc.
Due to the simple syntax of Python programming, programmers have often found that it is easy to learn. The learning curve of Python is relatively simple compared to other programming languages. If you have prior experience with coding in programming languages like C++ and JAVA, learning Python will not be difficult for you.
Python is more useful for programmers when trying to build software from scratch. And lastly, Python is used more by programmers and software developers.
R is a statistical programming language used for data analysis. Moreover, the R programming language can also play an essential role in data exploration and experimentation. Another difference between R and Python is that Python has a wide range of libraries. R language hasn’t got a wide range of libraries. Moreover, the fewer libraries of R are not that complex to learn.
Compared to Python, R has a relatively complex syntax that the user might find difficult to learn. A majority of the programmers believe that naturally, they were more comfortable with Python, compared to the R programming language.
R programming language is useful for working on complex mathematical and statistical problems encountered in data science and data analysis. It’s also believed that the statistical packages of Python are considered less resourceful compared to the R programming language.
Also, the R programming language is mainly used by research, development executives, and scientists who constantly need to experiment and analyze big data for insights.
These are the differences between Python and R.
Both languages are useful and in-demand skills. Python and R are two programming languages that are useful in data analysis. Both languages are useful in different industries. Python is considered better and more viable when it comes down to business use cases. On the other hand, for research purposes, R is considered a better programming language.
Now, which programming language should you choose while becoming a data analyst?
Python or R?
The answer is both languages have their pros and cons. If you think you need to analyze big data, work on machine learning algorithms and analyze data with ease you can go with Python. Moreover, if you are transitioning from a non-technical background and think you need a programming language that would be easy to learn compared to other languages, you can go with Python.
However, if you aim to get better at statistical analysis, R can be a better option. R has been developed by statisticians and it can come in handy for analyzing datasets. It is believed that if you have a background in statistics, learning R should be comparatively simple for you. Even if you don’t have prior experience in statistics, once you learn the basics of R, you will get along well with the language.
Both languages have been around for a while, and their programmers use those languages extensively. However, it’s you who must decide the purpose of learning the language. When you have clarity in your head, you will be able to decide what’s best for you.
To conclude, both Python and R are great programming languages to learn. Both languages are in-demand and considered essential for exploring and analyzing data.
If you wish to learn Python programming online for your career you can look for online courses which follow a project-based approach and cover all the concepts from the basics to the advanced.
There are good quality courses available online that make Python programming for beginners and intermediates easy and intuitive. Technology is moving ahead at a rapid pace, and programmers have found that both languages can be used in the workplace. You are the difference maker as your choice will decide what suits you better. If you are learning the languages to enter the data industry, it will also be essential to see which tools and programming languages your colleagues are using. After all, you will need to work with your team.
You may also want to read,
- What Coding Language is the Most Apt for Big Data Scientists
- The Ultimate Guide to Learning Python Programming Online
- Why Employing a Java Development Firm Remain Significant in 2023?
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