R vs. Python for Data Science: Who Wins?

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Python and R are the two most popular programming languages used by data analysts and data scientists. Both the programming languages are free and open source. Today, we discuss what makes one different from another and how to choose between these two languages.

Python Programming Language for Data Science:

  1. Python, a general programming language, turns out to be extremely flexible for things that have never been done before.
  2. It provides a more general approach to data science.
  3. It increases productivity along with increased code readability.
  4. It is extremely scalable for the use in data science.
  5. One of the reasons for growing success of Python is the availability of data science libraries, and their continuous updates.
  6. Because of the simple syntax, coding and debugging is easier.
  7. It can be used for scripting as well.
  8. It is mostly preferred by beginners because of its easy-to-learn feature.
  9. It is efficient in pulling the data, running automated analysis, producing visualizations like maps and charts from the results.
  10. It does not come with surprises and is quite predictable.

R Programming Language for Data Science:

  1. It is a programming language created by statisticians and is preferred for statistical analysis.
  2. R emphasizes on user-friendly data analysis, and graphical models.
  3. It is easier to use complex formulas in R.
  4. R Programming Language is preferred by experienced programmers, as beginners find it a little intimidating.
  5. R is chosen for data science because of its powerful Integrated Development Environment (IDE), and its accessibility from other programming languages as well.
  6. It has an extensive library of tools for database manipulation and wrangling.
  7. R has many tools that can help in data visualization, analysis, and representation.
  8. It provides almost every tool that a data analyst requires to manipulate and evaluate the data.
  9. It is perfect for analysts whose focus is on statistical methods.
  10. R is more suitable if you need to write a report and create a dashboard.

To an extent, both languages are pretty similar to each other. In fact, if you want to make a career in data science or data analysis you should pick one right away. If you are still not sure which language to choose, you should probably ask yourself if you want to learn the algorithm or deployment of the model.

If your answer to both the questions is yes, you should start with learning Python. Python includes great libraries to manipulate matrix and to code the algorithms. As a beginner, it might be easier for you to learn how to build a model from scratch. If you already know the algorithm and want to go into data analysis, both R and Python are fine to begin with. If you are interested in something more than statistics, Python is a better choice.

The final decision between R and Python depends on your aspirations, the amount of time you are ready to invest and the tools that are being used in your current project.

At Multisoft Systems, they have various courses as per your requirements including Learning Python Data Analysis, Python® Programming Certification Training, Data Science with Python Training and R Programming Certification Training.

About the author: Nisha Negi is a Technical Content Writer at Multisoft Systems. She writes blog posts and articles on various technical subjects. She is an experienced IT professional and bears immense knowledge of the latest technology. She stays current with all the ongoing and upcoming certifications. Her way of expression is contemporary and crisp.

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