Logo

Comparing Data Science Tools - Python vs R vs SAS

August 15, 2022

Comparing Data Science Tools - Python vs R vs SAS

Are you struggling to decide which data science tool to choose for your cloud orchestration project? Look no further, as we compare three popular data science tools - Python, R, and SAS - to help you make an informed decision.

Python

Python is a high-level, general-purpose programming language that is widely used in data science due to its simplicity, open-source libraries, and ease of use. Some popular Python data science libraries include NumPy, Pandas, and Scikit-learn.

Python offers a plethora of data visualization tools like Matplotlib, Plotly, and Seaborn, making it ideal for analyzing large datasets and creating visually appealing charts and graphs.

Pros

  • More than 200,000 libraries available via PyPI
  • Supports both scripting and object-oriented programming paradigms
  • A vast, supportive community that offers plenty of resources

Cons

  • Performance may not be optimized as it takes time to process large-scale datasets.
  • Multi-threading and parallel processing with Python can be tricky

R

If you're analyzing statistical data, R is an ideal data science tool for you. It is an open-source programming language that specializes in data analysis and modeling. R is equipped with several statistically oriented packages, including dplyr, purrr, and ggplot2.

R is a top choice for a wide range of data scientists, ranging from biologists, physicists, and statisticians due to its essential functionality, syntax, and visualizations.

Pros

  • Optimized for statistical computations and data visualization
  • One of the most popular data manipulation languages
  • Rich in data mining and machine learning capabilities

Cons

  • Steep learning curve required to master
  • Limited commercial vendor support

SAS

SAS is a premium data science tool that is widely used in business intelligence, data analytics, and statistical modeling. It offers a user-friendly interface that allows analysts to perform complex analytics without coding.

The SAS system has advanced graphical capabilities and analytics functions like Forecast Server and Marketing Optimization which are widely used in industries like finance and healthcare.

Pros

  • The SAS system has advanced graphical capabilities and analytics functions
  • The SAS software suite is widely used in Data Science competitions
  • Offers training & certification to its users through SAS Academy.

Cons

  • Premium certificate and license required for its usage
  • Limited free online support forum

Conclusion

In conclusion, each of these data science tools has its merits and demerits. It is best to consider the scale of your project and the specific task you need the tool to accomplish. If you’re looking for an easy-to-use language and a vast community to support you, Python is the way to go. If, on the other hand, you want a language optimized for statistical computations, R is the way to go. If you are looking for industry-standard software and don't mind paying a premium for them, SAS is your best option.

Be sure to try out each of these data science tools to determine which one meets your requirements before making a final decision.

References

© 2023 Flare Compare