Python and R both show up constantly in research methods discussions, and both are entirely capable of handling serious academic data work. The right choice depends less on which language is objectively superior and more on what your field expects and what your specific project needs.
What Python is best at
Python is a general purpose programming language that happens to have excellent data science libraries, which makes it a strong choice if your research involves machine learning, web scraping, automation, or building tools alongside your analysis. Its readability also makes it a comfortable entry point for students with no prior coding background.
What R is best at
R was built specifically for statistics, and it shows. Its statistical packages are often more specialized and easier to use correctly for classical hypothesis testing, regression modeling, and academic style visualization than their Python equivalents. Many social science, biology, and public health departments have built entire research cultures around R specifically because of this statistical depth.
Learning curve for complete beginners
Python is generally considered slightly friendlier for absolute beginners, thanks to its clean, readable syntax and its use well beyond academia, which means abundant tutorials exist for nearly any problem you encounter. R has a steeper initial learning curve for some concepts, particularly around its data structures, but students who need it primarily for statistical modeling often find its statistical functions more intuitive once past that initial curve.
Community support and available packages
Both languages have enormous, active communities. Python's package ecosystem spans far beyond data science into web development, automation, and general software engineering. R's package ecosystem, centered around CRAN, is deeply focused on statistics and specialized analysis methods, meaning that for many niche statistical techniques, someone has likely already built and published an R package specifically for it.
Which one fits your specific field
If your field is computer science, engineering, or increasingly economics and finance, Python is often the expected standard, particularly if your research touches machine learning. If your field is psychology, biostatistics, epidemiology, or much of social science, R remains extremely common, and your department's existing norms, published papers, and available supervisor expertise should weigh heavily in your decision.
Can you realistically learn both
Many working researchers eventually learn enough of both to move fluidly between them, using each where it is strongest. If your timeline allows it, starting with whichever language your department or supervisor already uses is the most efficient path, since you will have immediate support when you get stuck, which matters more in the early weeks than any theoretical advantage of one language over the other.