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They might be considering which programming language to learn, like many data scientists. What you desire from a programming language will determine which one you choose. Due to the extensive libraries and packages available for R and Python, some of you may feel overpowered by their unique features. This post was written with you in mind. 

First of all, we will discuss your purpose using the programming language which is R and Python. 

Who uses R?

R is a platform for statistical computing. It not only has classical tests, time-series analysis, clustering but also has many packages and layers for plotting and analysing graphs, such as ggplot2. That’s why R is popular not only with data scientists but also with statisticians and people in other fields needing to manipulate data

Who uses Python?

Python has tools for machine learning, neural networks and Tensorflow. Besides its libraries include NumPy for statistical analysis, pandas for data preparation, and seaborn for generating plots.  With all of its features, Python is an excellent tool for programmers and developers as software development, automation and robotics,… can across a wide range for developing many algorithms The algorithms could simulate biomolecules or deliver anti-spam software. 

Let’s compare R and Python

We have some main things that can be considered in choosing the programming language, it would be: Online community, Difficulty to learn, User-friendly interface and Users. Now let’s take a look at the table below so that we can compare R and Python with these features above

Comparision between R and Python

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The Strength of R and Python
Both R and Python offer you different opportunities to create smart coding with minimal effort. While they have some similarities, each has its own set of strengths. R Studio:
  • Having over 10,000 packages for data wrangling on its CRAN -> perfect to manipulate data
  • Making beautiful, publication-quality graphs very easily. R lets users alter the aesthetics of graphics and customise with minimal coding.
  • Allowing various visible exports and formats. (interpreter-based)
  • Spotting bugs much easier (based on long experience with other languages
  • Powerful tool is its statistical modelling, creating statistical tools for data scientists
Python:
  • Easy and intuitive to learn for beginners
  • Appealing to a wide range of users. This creates a growing community in more disciplines
  • Set-up for python is easier than for R
  • Will be strict with users on Syntax. Python will refuse to run if u haven’t met easily missable faults -> Makes us better, neater code writers.
  • Faster at dealing with large datasets, making it more appropriate for big data handlers

You might decide that it would be preferable for you to eventually learn both skills, depending on your career, your interests, and your needs.  As you come to know each for their unique abilities, it’s at the very least helpful to know enough to be able to interpret the other’s syntax. You will surely benefit from more doors opening and job placement thanks to this.