Economists use both R and Python, along with Stata, depending on their specific tasks, with a growing trend towards "polyglot" skills. R is preferred for advanced statistical modeling, econometric research, and high-quality visualizations. Python is favored for machine learning, data engineering, and handling large datasets.
For students solely interested in economic research, R may be the more accessible option, but those with broader programming ambitions might find Python a better long-term choice.
The most widely used programming languages for economic research are Julia, Matlab, Python and R.
The learning curve for Python is smooth compared to R. R has a steep learning curve. Python is easier to learn and implement.
Large financial firms typically use multiple languages across different functions. Python is common for data analysis and some quantitative research, while R is widely used in risk management, econometric research and regulatory reporting.
Among these revered instruments stands R, a language designed not just for computing, but for comprehending. As the digital cosmos expands in 2025, R remains a luminary for statisticians, data scientists, and analysts seeking a language built from the ground up for the rigorous examination of data.
For CFA® Level I candidates in 2024, two options are available: Financial Modeling and Python Programming Fundamentals.
No, R isn't being replaced by Python; they are both powerful tools that excel in different areas, though Python is growing faster in general data science and ML due to its versatility in production, while R remains dominant in academia, biostatistics, and advanced statistical analysis with superior visualization (like ggplot2) and statistical packages, with many professionals learning both. Think of them as complementary tools: Python for broader applications and deployment, R for deep statistical dives and research.
The 80/20 Rule in Python Codebases
This means that performance optimization should not be applied evenly across the entire codebase. Instead, developers should identify the critical 20 percent of code that consumes most of the runtime and optimize that part first.
Python, MATLAB and R
All three are mainly used for prototyping quant models, especially in hedge funds and quant trading groups within banks. Quant traders/researchers write their prototype code in these languages.
SecDb is an ecosystem within Goldman Sachs which consists of object-oriented database technology, and data synchronizing mechanism. Also, the other core components of the SecDb are a language which we'll be discussing called Slang, which is a scripting language.
How long does it take to learn R programming? Generally speaking, people can usually learn the basics in as little as three to six months, while learning more advanced concepts can take closer to a year or more.
Elon Musk's iconic company, Tesla, operates on an operating system built on the Python programming language. Elon Musk continues to prefer it as his favourite programming language.
Instead, the choice depends on your needs: Choose R if your focus is statistical modeling, visualization, or reproducible reporting in research and applied analysis. Choose Python if you need a versatile language for machine learning, AI, and production systems with wide industry adoption.
Top 7 Most In-Demand Programming Languages for Finance and...
Both open source programming languages are supported by large communities, continuously extending their libraries and tools. But while R is mainly used for statistical analysis, Python provides a more general approach to data wrangling.
Yes, two months is enough time to learn Python basics and even some intermediate concepts, especially with consistent, focused effort (e.g., 2-4 hours daily), allowing you to write simple programs and understand core syntax, though becoming job-ready for complex roles takes much longer, involving libraries, frameworks, and real-world experience.
Despite the availability of faster languages, more modern syntax, and better tooling in other ecosystems, Python continues to be the first choice for new projects across multiple domains. That's not the behavior of a dying language.
New programmers who have no coding experience commonly appreciate Python's ease of use. R is a more specialized language that is considered more complicated to learn, with its unique syntax, steeper learning curve, and potentially confusing commands.
Answer: No, AI will not replace R developers. Their expertise is critical for designing complex statistical models and custom data analysis workflows.
Python is an incredibly versatile language with a very simple syntax and great readability. It is used for building highly scalable platforms and web-based applications, and is extremely useful in a burdened industry such as finance.