If you are faced with a choice — study R or Python for data analysis, you are not alone. In this article, we will help you understand the differences between these two languages, which one is right for you, and how to start learning effectively.
In 2025, the choice for a data analyst still revolves around the two strongest languages — R and Python. And each has its own pros, approaches and fans. Below is an honest comparison for beginners and anyone who wants to learn.
🧠 A little history
R — a language created for statistics, popular in scientific and academic circles.
Python — a universal language that has become extremely popular in the field of data science.
📈 When to choose R
You come from an academic or research background.
Advanced statistics and high-quality visualization are needed.
You want to work in RStudio and build graphs quickly (for example, using
ggplot2).
🐍 When to choose Python
You want a universal tool: data analysis, web, ML, automation.
You plan to work in an IT company.
You want to study neural networks, use TensorFlow, Pandas, scikit-learn.
📊 Visualization
R is famous for its visualization — ggplot2, lattice, and plotly libraries allow you to build clear and beautiful graphs in just a couple of lines. Everything is tailored for analytics and presentation of data "out of the box".
In Python visualization is also powerful, but more often requires more code and fine-tuning: here matplotlib, seaborn, plotly and others are used.
🤖 Machine learning
If you want to train models, build forecasts, or develop neural networks, Python will be a more versatile choice. It has a huge ecosystem for ML: scikit-learn, TensorFlow, PyTorch. But you can also start confidently in R: the caret, mlr3, and tidymodels libraries are great for training models and experimenting with data.
🧪 What is easier for a beginner?
Python is easier in terms of syntax.
R is more mathematical, but is great for those who work with data and tables.
🧭 Let's look at the table
Your goal | Selection |
|---|---|
Analysis, statistics, visualization | R |
Machine learning, neural networks | Python |
You want versatility and automation | Python |
Study, science, research | R |
✨ The main thing is to start
Both languages are excellent. Choose one and get started. In a couple of weeks, you will better understand the data and will be able to build your own charts, reports, and models.
In Kodik app you can try R in practice: solve problems, create graphs and build simple data applications in R. Everything in a convenient and understandable form, without overload and with care for beginners.