C and C++ are proving to be efficient when it comes to data science applications.

The world of data science usually resides in high-level, declarative programming languages. Python is one such example among the various programming languages, that is extensively used in data science. But recently, experts have found out that C and C++ can become strong choices for efficient and effective data science applications. The number of big data applications on the market is growing exponentially, therefore, data scientists are considering different types of programming languages to increase the efficiency of these applications.

Initially, Python has been the most preferred programming language by data scientists. A survey found that 66% of data scientists used Python to create their applications. R had been a popular programming language, back in the day, but Python quickly became more appealing for a variety of reasons. However, developers have started to recognize other languages, such as C++ and C, that are providing various other opportunities for data science development. In some ways, C++ and C are the best programming languages for larger data projects.

C for Data Science

C is a general-purpose programming language that is among the best programming languages available today. It is quite an old program, and several other programming languages have been written on it. It is one of the closest languages to the inner workings of the computer as it can manipulate memory directly. C is also a very fast language to compile. This is specifically proven when it is compared to the other options for data science. This also makes C an ideal option for implementing machine learning algorithms that generally takes a lot of time to process.

One of the primary reasons why C is proving useful for data scientists is because it is a very compiled language. The source code has to be translated by a compiler into machine code. Its standard library is small and light on features, so other libraries have been developed to compensate for the missing functionalities.

How does C++ contribute?

C++ has very rapid processing capabilities. When it comes to developing big data applications, the speed of the compiler is one of the most important features. Therefore, C++ proves an excellent option as a data science programming language. It is the only programming language that can be compiled over a gigabyte of data in less than a second.

Several professionals think that programming languages are a lot more fragmented than they actually should be. C++ has emerged as a program that is bridging the gaps between other languages in the library. Since data science is growing reliable on other programming libraries, C++ may prove efficient in this aspect.

Most of the modern programming languages are built on C and C++, therefore, there are several similarities between these two languages and other object-oriented programming languages. So, developers try to replicate the code with other programming languages, such as Python, so that they need to make lesser changes while integrating it with other algorithms.

With data science, programmers have to be extremely knowledgeable about language ecosystems. While several other programming languages can be more efficient and accessible for data science professionals, C and C++ are proving handy in different other activities. Professionals using Python and R, might eventually become aware of the advantages provided by these languages and integrate them with data science and AI technologies.