For a growing number of people, data science is a central part of their job. Increased data availability, more powerful computing, and an emphasis on an analytics-driven decision in business has made it a heyday for data science. According to a report from IBM, in 2015 there were 2.35 million openings for data analytics jobs in the US. It estimates that the number will rise to 2.72 million by 2020.
The two most popular and best programming languages for AI work are Python and R at the moment (many surveys have found this to be true, along with this Data Science Survey conducted by O’Reilly). It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and open source and were developed in the early 1990s — R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.
There are a lot of studies available comparing the adoption and popularity of R and Python. While these figures often give a good indication of how these two languages are evolving in the overall ecosystem of computer science, it’s hard to compare them side-by-side. The main reason for this is that you will find R only in a data science environment. Python, on the other hand, is widely used in many fields, such as web development. This often biases the ranking results in favour of Python.
In terms of the ability to build and develop an AI system, both R and Python have enormous and reliable libraries. But they differ in some other relevant aspects:
- Widely used, especially in Academia and Research. Statistical models and complex formulas can be written in a few lines. Makes it a great fit for AI modelling.
- Huge community with lots of support.
- In visualization libraries, R wins hands down.
- The worst thing about R is that it was developed by statisticians and hence has a big learning curve and is annoyingly unintuitive.
- It is not easy to integrate R code into other systems, which is why it is not used a lot in the industry today.
- Much more intuitive and user-friendlier than R, loved by programmers.
- It’s a full-fledged programming language, so it’s easy to implement and integrate ML/AI systems for production use.
- Some of the modules of R which can be of great value in ML algorithms aren’t available or have no replacement in Python.
In certain situations, one language or the other may be better suited for a certain situation or use case, or perhaps even a different language such as Scala (for Spark), C++, etc.
Data Modelling is really important for ML and AI. It forms the base of everything the algorithm learns. And judging the languages in question based on this factor is important. Best programming languages for AI need to have great data modelling capabilities. In python, you can do numerical modelling analysis with Numpy. You can do scientific computing and calculation with SciPy. You can access a lot of powerful machine learning algorithms with the scikit-learn code library. scikit-learn offers an intuitive interface that allows you to tap all of the power of machine learning without its many complexities.
In order to do specific modelling analyses in R, you’ll sometimes have to rely on packages outside of R’s core functionality. There are plenty of packages out there for specific analyses such as the Poisson distribution and mixtures of probability laws.
Before choosing a language, you might want to ask a few questions
1 — Do you have experience programming in other languages? If the answer is yes, Python might be the language for you. Its syntax is more similar to other languages than R’s syntax is (as mentioned earlier). Python is easy to read, like a verbal language, which emphasizes development productivity, while R’s unstandardized code might be a hurdle to get through in the programming process.
2 — Do you want to learn “machine learning” or “statistical learning”? Machine learning is a subfield of Artificial Intelligence, while Statistical Learning is a subfield of Statistics. Machine learning has a greater emphasis on large-scale applications and prediction accuracy; while statistical learning emphasizes models and their interpretability, and precision and uncertainty.
Since R was built as a statistical language, it suits much better to do statistical learning. Python, on the other hand, is a better choice for machine learning with its flexibility for production use, especially when the data analysis tasks need to be integrated with web applications.
3 — Do you want to visualize your data in beautiful graphics? For rapid prototyping and working with datasets to build machine learning models, R inches ahead. Python has caught up some with advances in Matplotlib but R still seems to be much better at data visualization (ggplot2, htmlwidgets, Leaflet).