What programming language should one learn to do well in machine learning or data science field? One can find a number of articles/blogs attempting to answer this golden question both based on personal experience and on job offer data. Features such as high salary, popularity in terms of current job openings, future demands and acceptance to learn influence the decision. Opinions also vary depending on career backgrounds and domains used. To decide the right language, it is also wise to think through kind of projects, background in mathematics/logic, desire to learn a high-level language, interest in job types (freelance, startup or regular job) and stage in career.
There are numerous programming languages aiming to solve the business intricacies and bring technological innovations. Not many programming languages and frameworks that emerge stand the test of time. Let’s go through a list of the top programming languages based on usability, features, popularity, job openings, pay that it offers, amongst others.
Python (Very relevant especially for high level analysis and Deep Learning)
As stated by KDNuggets, Python is perhaps the most popular and user-friendly programming language in the field of data science and machine learning. Python’s clear, intuitive and almost English syntax makes it a popular choice for beginners. Python’s use case is versatile. Its open-source Django and Flask framework is useful in back-end web development. It is also easy to learn and feature-rich. Python also has numerous packages such as NumPy, Pandas and SciPy that are commonly used in the fields of scientific computing, mathematics and engineering. Other Python libraries such as TensorFlow, PyTorch, scikit-learn and OpenCV are used to build programs in data science, machine learning, image processing and computer vision. Python’s science and data applications make it a great choice for the academically inclined. With a rise in technologies like machine learning, artificial intelligence and predictive analytics, the need for professionals with a thorough knowledge of Python skills are much in demand.
R (Very relevant especially for high level analysis, used mostly by statisticians and less in AI/Deep Learning)
R has been increasingly gaining enterprise adoption over the last few years. It has become widely demanded skill sets across recruiters in data science and machine learning. It is used to unlock the patterns in large blocks of data. It was designed by statisticians and scientists to make their work easier. Environments have been built around R in order to make it feasible for enterprise deployments. R Studio has been integrated initially with Apache Spark and now with Databricks. R is more prioritized in the field of sentiment analysis, bioengineering and bioinformatics.
C/C++ (Very relevant for doing performance critical work for ML)
C has had a prevailing influence on the computer programming landscape. C is a common choice for building specialized high-performance applications. It is best suited for writing assembly-level code for different types of architectures (x86-64/PPC/ARM) or GPU related code. With only 32 keywords, it has probably had the most profound impact on computing. C is the basis for the Linux, Windows, Unix etc. operating systems and is frequently used for programming embedded systems.
C++ is the direct sequence of C. C++ builds on C, which gives it many of the same advantages, but C++ is an object-oriented language and is a better choice when developing higher-level applications. C++ is a popular choice for computer graphics, video games and virtual reality.
A large number of systems have been created and maintained effectively using C++, including the likes of Microsoft, Oracle, PayPal, and Adobe. Artificial Intelligence (AI) in games, computer graphics, video games, virtual reality and robot locomotion are the areas where C/C++ is favored the most, given the level of control, high performance and efficiency required. C/C++ that comes with highly sophisticated AI libraries is a natural choice. C can be a good starting point for equipping yourself with other languages.
Rust (New and relevant in AI/ML)
Rust is a relatively new language which has already gained notable popularity and is expected to improve even further in the future. Stack Overflow’s 2018 Developer Survey found that Rust was the most loved programming language among developers for the third year in a row, with 78 percent of Rust developers saying that they want to continue working with it. Rust, like C and C++, is intended primarily for low-level systems programming. Rust emphasizes speed, security and writing “safe code” by averting programs from accessing parts of memory. Big tech companies, such as Dropbox and Coursera, are using it internally. For a beginner, Rust might be a bit of a struggle to pick, however, Rust programming skills are likely to pay off handsomely as the language’s popularity will only continue to rise in the near future.
Julia (Moderately relevant for AI/ML)
Julia is quickly gaining momentum amongst the data scientists. It is a high-level dynamic programming language and is designed for high-performance numerical analysis. The base library in Julia is integrated with open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. A collaboration between Jupyter and Julia communities, it provides a powerful browser-based graphical notebook interface to Julia.
Swift (new in mobile app machine learning, Mostly for frontend)
Developers use Swift to build powerful, high-performance, native iOS, macOS and Linux apps. Swift is intended to be a faster, more streamlined and easier to debug than its predecessor Objective-C. Developing Swift programming skills is a wise investment for aspiring software engineers. Not only does iOS run on every iPhone and iPad, it’s also the basis for other operating systems such as watchOS (for Apple Watches) and tvOS (for Apple TVs). With the release of several new software development frameworks – Swift for TensorFlow and Apple’s own Core ML 2 and Create ML – swift has a great future in mobile app machine learning. If you want to get into mobile development you should definitely consider Swift as a high-paid career path.
SQL (mosty for database operations, recent advances in machine learning):
SQL is one of the favorites amongst the data science gurus. It has been widely used for storing and retrieving data for decades. With a SQL database, the database itself is storing the data and processing the data, so by using SQL operations, you collapse the stack for performance and scalability. SQL is easy to learn and widely deployed in enterprises. Learning SQL can be a good addition into skills required for data science and ML experts, as this is looked after by most recruiters as a preferred skill set.
So far, we discussed a number of programming languages that were popular to learn in 2016 and 2017. They will potentially continue to be popular for the next several years as well. However, popularity should not be the only criteria in choosing a programming language for machine learning and data science. It is hard to say a particular programming language is the ‘best language for machine learning’. Basically, it depends on what you want to build, where you’re coming from and why you got involved in machine learning.