I'm starting from nearly zero and want to learn programming purely for fun, not to pursue a career. I'm especially curious about artificial intelligence, machine learning, data science, statistics, data visualization, and scientific computing. R appeals to me because it is built around statistics and data analysis, while Python seems more general-purpose and Julia looks interesting for numerical and scientific work. I'm not choosing based on professional popularity. For someone learning mainly out of curiosity, is R a good way to learn programming fundamentals, or would Python or Julia provide a stronger foundation? I'd especially like to hear from people who have learned or used R or Julia as a hobby.
5 Answers
For AI and machine learning, Python is probably the most practical starting point. It has a huge collection of beginner resources and libraries, and it can handle general programming as well as data analysis. It may not be the absolute simplest language in every detail, but it gives you room to build small programs, automate tasks, work with data, and eventually explore machine learning without switching languages immediately.
Whichever language you choose, the important step is moving beyond tutorials. Learn the basic syntax, write small programs without copying every line, and then make a tiny project. Python is the broadest starting point for your interests, while R may be more immediately satisfying if you mainly want to analyze and visualize data. Since this is a hobby, you’re allowed to try one, switch, or learn both—there isn’t a wrong choice.
R is still a perfectly reasonable choice if your main goal is exploring data. A small project—such as loading a CSV file, cleaning it, calculating some summaries, and creating a plot—could be a fun way to learn variables, functions, conditionals, and iteration in a meaningful context. Its vectorized and data-frame-oriented style hides some lower-level details, though, so it may not expose as much of general programming as Python or a lower-level language.
R is excellent for statistics, data frames, visualization, and modeling, but it isn’t usually the best first language for learning general programming. Its syntax and behavior have some unusual quirks, and it tends to encourage a specialized data-analysis workflow rather than teaching broader ideas like data structures, system programming, and software design. If you’re especially interested in statistics, you can absolutely start with R, but Python is a safer general-purpose foundation. You can always add R later and will probably appreciate its strengths more once you know another language.
That makes sense. I’m leaning toward starting with Python and trying R afterward, especially for visualization and statistics.
Julia is appealing for numerical and scientific computing, but I wouldn’t make it my first language unless you already have a specific reason to use it. The ecosystem and beginner material are smaller than Python’s, so it can be harder to find examples and help. It’s a good language to explore later, particularly if you become interested in performance, numerical methods, or parallel computing.
Python has quirks too, but the amount of documentation and examples available makes it much easier to get unstuck as a beginner.