R vs Python – Which One To Choose For Data Science?

R vs Python – Which One To Choose For Data Science?

Python and R are known to be the most popular programming languages for data scientists. For a solid programming foundation, you should ideally be able to grasp both, but if you're new to data science, where should you begin?

Continue reading to find out more about the applications of each programming language in data science and get advice on which one to start learning first.

What’s the Difference Between R and Python?

What’s the Difference Between R and Python?

Python is a general-purpose coding language created for a multitude of use cases, in contrast to the more specialist R language.

If you're new to programming, Python code might be simpler to understand and more widely used. R language may be more suited to your needs, though, if you already have some familiarity with coding languages or have specific professional objectives focused on data analysis.

Additionally, Python and R share many commonalities, making knowledge of one useful for understanding the other. For instance, R and Python are both well-liked open source programming languages supported by active communities. In addition to other programming languages like Julia, Java, Scala, and dozens more, both can be practiced in the language-neutral environment called Jupyter Notebooks.

The All-Purpose Programming Language - Python

Like Javascript or C++, Python is an object-oriented coding language that gives all projects, regardless of size, stability, and modularity. It provides a versatile approach to website development and data science that seems natural even if you've never used a programming language before.

Learning Python equips programmers with the abilities needed to work in fields other than data science, including business, digital products, open-source initiatives, and numerous web applications. The Python ecosystem includes the following well-known libraries, which are used by the language:

  • NumPy
  • SciKit-learn
  • Keras
  • SciPy
  • Seaborn
  • Folium
  • Pandas
  • Matplotlib
  • PyCharm

Why Learn Python for Data Science?

Beginner-friendly

Python depends less on the formalized approach of earlier languages and instead employs a logical and approachable structure that makes it simpler to understand the purpose of strings of code. By emphasising code readability, programmers can overcome some of the difficulties associated with learning new programming languages and shorten their learning curve.

Multipurpose

Python is used for more than only data science projects. Python is a useful language to utilise if you intend to concentrate on a variety of activities within the computer science sector, as developers use it to build all types of programs. Python also supports a wide range of data structures, including those with SQL, and is effective with web-based applications. Additionally, it's simple to locate various datasets for any project you're working on or to develop your own utilising tools from the Python ecosystem.

Scalable

Python runs more quickly than R, enabling it to expand and scale with projects. It provides the effective workflows required to launch those for those engaged in production, pipeline construction, or large-scale production. The base of Python's production suitability is its speed. Building comprehensive ML pipelines for insights that keep up with the pace of business is possible. Additionally, the language's modularity makes it possible to construct flexible systems.

Why Learn R Programming for Data Science?

Built for Statistics

With Python, you can perform intensive statistical analysis, but you won't have access to R programing's syntax-specific libraries and functions. The language makes it considerably easier to design and communicate applications of this type and their outcomes. R programming helps statisticians and data analysts more readily manage massive datasets using common ML models and data mining techniques.

Academic

R programming use is practically required for academic work. The statistical learning branch of machine learning is ideally suited to R. Anyone with formal statistical training should be familiar with R's syntax and structure.

Intuitive for Analysis

R programming is the greatest option for analysis and inference tasks, even though it may not be compatible with a wide range of applications. You should use a specialist programming language if you intend to work in a specialised field. R programming provides a strong environment that is perfect for the kinds of data visualisations used by data scientists.

The Data Analysis Powerhouse - R

R is a programming language that is used for statistics and data analysis. It employs specialised syntax used by statisticians and is a crucial component of the field of academic and research data science.

R develops according to a procedural model. It divides programming jobs into a sequence of steps and subroutines rather than organising data and script into groups like object-oriented programming. It is easier to imagine how complex operations would work thanks to these techniques.

R has a strong community, similar to Python, but with a particular emphasis on analysis. R doesn't provide general-purpose software development such as Python, but because it solely focuses on data science, it manages these specialised projects better. The R ecosystem consists of:

  • RStudio
  • CRAN
  • Tidyverse, a popular collection of R packages
  • dplyr
  • R packages, reproducible R codes, and functions
  • Ggplot2, an open source data visualization package

Conclusion

Python is the best choice if your objective is to learn computer programming more broadly. R may have the advantage if your objective is to only use it for applications involving statistics and data. Ask yourself the following questions to determine whether Python or R programming should be learned first:

  • What do you want out of your career? Making a choice between business and education, for example, can reveal which will benefit you more initially. It can also be helpful to consider how much you want to leave your options open or which initiatives are most essential to you.
  • Where do you think you'll focus the most of your efforts? R may beat Python if you intend to stick with statistical analysis for the majority of your research endeavors. However, you might require greater flexibility if you want to create systems that are ready for production.
  • How are you going to present your findings? Your first step can be made more focused by taking a look at the various ways Python and R can help with data visualisation.

Subscribe to ConnectingDots Infotech

Don’t miss out on the latest posts. Sign up now to get access to the library of members-only posts.
jamie@example.com
Subscribe