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The concept of big data has been around for years. Today most organizations understand that if they capture the data that flows into their businesses, they can apply analytics to make smarter business moves, organize operations more efficiently, get higher profits and happier customers.

The practice of data science requires the use of analytics tools, technologies, and languages to help data professionals extract insights and value from it. Today data science experts have an extremely wide range of various tools available which they can use to cope with literally any issue they might face. However, today we want to cover two the most popular programming languages, which are commonly acknowledged as the best tools for data science projects: R and Python.

R is a programming language used for data manipulation and graphics. R became extremely popular tool among data science experts as it has thousands of extension packages that allow statisticians to undertake specialized tasks, including text analysis, speech analysis, and tools for genomic sciences. R is an open-source ecosystem that allows programmers to create additional add-on packages for handling big datasets and practice parallel programming techniques that are gradually dominating statistical modeling today.

Python is a very powerful general-purpose programming language that focuses on readability and simplicity of code. Python is widely used for many different applications as it has robust libraries that support statistical modeling, data mining, and visualization. Just like R, Python is an open source language with a great community that has created a wide range of tools for efficient work on data projects.

Let us now compare R and Python languages on different criteria:

R: Pros and Cons

Pro: outstanding graphical capabilities.

Pro: active community and rich ecosystem of cutting-edge packages.

Pro: suitable for GNU/Linux and Microsoft Windows. R is a cross-platform language and can be run on many operating systems.

Con: R has the steepest learning curve, so it becomes necessary to learn coding. It’s a low - level language, so simple procedures can take longer codes.

Python: Pros and Cons

Pro: Python is a general purpose language that is easy and intuitive with a relatively flat learning curve what gives you a chance to spend less time to code and increase the speed at which you can write a program.

Pro: multi-purpose language so you can build a single tool that easily integrates with every part of your workflow.

Pro: easy integration with extensible using C and Java

Pro: supports multiple systems and platforms.

Con: not good for mobile development.

Con: although Python has some good visualization libraries, compared to R, visualizations are usually more convoluted, and the results are not always so visually good and explicit.

Who is the winner?

This entirely depends on you and specifications of the project you work on. As a data scientist it’s your privilege to select the language that best fits your needs. Some questions you can ask yourself while considering the tool to use:

  • What problems do you want to solve?
  • What are the net costs for learning a language?
  • What are the commonly used tools in this field?
  • What are the other available tools and how do these relate to the commonly used tools?

If you’re planning to start a career in data science, you are good with both languages. If you are a data expert and looking for tools to develop your professional skills, good luck!