Data science has become one of the most transformative disciplines of the 21st century, enabling data-driven innovation across domains. Among the tools power- ing this revolution, Python and R stand out as the most popular programming lan- guages used by researchers and industry professionals. This paper presents a com- parative study on the efficiency and applicability of Python and R for data science tasks. The study surveys 25 recent research papers, analyzing the core strengths, performance metrics, and domain-specific applications of both languages. Exper- imental validation using real-world datasets (Iris, Titanic, COVID-19 time series) highlights comparative results for model building, execution time, and visualization performance. Findings indicate that Python demonstrates better integration and scalability, while R exhibits superior statistical depth and visualization capabilities. The work concludes with insights on selecting the appropriate language depending on project requirements and suggests directions for hybrid approaches combining both ecosystems.