PYTHON AND R: A SIDE-BY- SIDE EVALUATION FOR ANALYTICS EXCELLENCE

Abstract [English]

• This paper compares Python and R Studio in the domain of data analytics, focusing on their capabilities, libraries, scalability, ease of use, and application suitability. Python, being a general-purpose language, is favored for machine learning and production systems, while R is tailored for statistical analysis and visualization. This study provides insights to help data scientists choose the appropriate tool based on specific requirements.

• The investigation focuses on a comparative analysis of Python and R in data analytics, particularly regarding their capabilities, ecosystems, scalability, ease of use, and application suitability.

• The analysis aims to determine the strengths and weaknesses of Python and R, helping data professionals choose the appropriate tool for specific requirements in data science and analytics.
• The study compared Python and R by evaluating their features, library ecosystems, performance, scalability, learning curves, and suitability for various applications. Sources included peer-reviewed articles, technical blogs, and industry whitepapers.
• Python is versatile and excels in data wrangling, machine learning, and production systems.
• R specializes in statistical analysis and visualization.
• Python has a broader application scope and better scalability for large datasets.
• R is preferred for statistical precision and high-quality graphics.
• Both languages have strong, complementary ecosystems.
• The findings reveal that Python and R are tailored for different purposes. Python is more suitable for general-purpose data manipulation and machine learning, while R is ideal for statistical research and detailed visualization. Combining their strengths can enhance data analytics workflows, emphasizing the importance of mastering both languages for maximum versatility