Welcome | R for Data Science (2024)

FAQs

Is learning R enough for data science? ›

Both languages are well suited for any data science tasks you may think of. The Python vs R debate may suggest that you have to choose either Python or R. While this may be true for newcomers to the discipline, in the long run, you'll likely need to learn both.

Although the time it takes to learn R depends on several factors, most individuals can become familiar with this coding language in about four to six weeks. You can receive comprehensive R programming training through Noble Desktop's in-person or live online courses.

R is a versatile language for any aspiring data professional or researcher, and by learning the integral skills, you'll develop a solid foundation for your data science journey.

As of August 2021, R is one of the top five programming languages of the year, so it's a favorite among data analysts and research programmers. It's also used as a fundamental tool for finance, which relies heavily on statistical data.

Is Python or R easier? Python is much more straightforward, using syntax closer to written English to execute commands. However, R makes it easier to visualize and manipulate data if you have other languages under your belt. It's statistics-based, so the syntax here is more straightforward for analysis.

Performing statistical analysis in R is a valuable skill for aspiring data analysts to learn in 2024. R provides a wide range of functions and packages that make it easier to prepare data and perform complex analyses.

For learners with programming experience, you can become proficient in R within a couple weeks or less. Brand new programmers may take six weeks to a few months to become comfortable with the R language.

The learning curve for R varies depending on the depth; one can get first-hand familiarization of the language in a few weeks; nonetheless, profound knowledge, which is required for using R proficiently in elaborate applications and project, may take from several months to a year or perhaps more.

There's no wrong choice when it comes to learning Python or R. Both are in-demand skills and will allow you to perform just about any data analytics task you'll encounter.

Although it's essential to look at some different programming careers and the languages they use regularly, R will open opportunities for you to pursue a career in several data analytics and statistics-based positions, such as data scientist, data analyst, data architect, statistician, or data engineer.

Should data science learn R or Python? ›

R programming is better suited for statistical learning, with unmatched libraries for data exploration and experimentation. Python is a better choice for machine learning and large-scale applications, especially for data analysis within web applications.

Approximately 38% of professionals use R for data science.

R is a great language for data cleaning, analysis, and visualizations. It is definitely not dead. In bioinformatics we use R regularly and for certain analysis there are only R packages available.

Conclusion — it's better to learn Python before you learn R

There are still plenty of jobs where R is required, so if you have the time it doesn't hurt to learn both, but I'd suggest that these days, Python is becoming the dominant programming language for data scientists and the better first choice to focus on.

Python is now overtaking R in it's usage by data scientists.

SQL is better at Data Management than R. R is better at Data Visualization than SQL. For data aggregation and complex data operations, SQL is way quicker than R. R is quicker than SQL for performing basic data querying and data manipulation tasks.

C++ provides better performance but has a steeper learning curve, while R is more user-friendly and intuitive for data analysis tasks.