If you are reading this, we assume you have already done your research about the fundamental skills required to be a data scientist. Apart from math and statistics, you should be very strong in your computing skills, if you want to pursue a career in this field. These skills involve knowledge of programming languages, coding, algorithms, Artificial Intelligence, Machine Learning, Business Intelligence and more.
When it comes to programming languages, the two most commonly used languages used for data science are Python and R. This gives rise to the question if you need to learn other languages, like C++, to become a data scientist. If you have also been thinking along the same lines, keep reading to know more about this.
C++ for data science
C++ is a low-level language used for apps that require maximum performance. These apps include gaming, web browser development and multiple operating systems. Thanks to its compatibility with advanced computational projects, it is used in data science projects that deal with complex numerical apps.
C and C++ are known for their speed. They are faster than most programming languages used for data science. This speed makes them useful for projects involving huge volumes of data and numerical information. You can also C++ extensively for machine learning and artificial intelligence concepts.
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You might already know that Python, being an open-source library has countless libraries, dictionaries and sets in it. A few of the most common libraries that data scientists use from Python are TensorFlow and Keras. As a data scientist, you should know C++ to access these machine-learning libraries, because these codes are written in C++.
Though knowing C++ can help you work on big and complicated data science projects involving advanced computing & numerical skills, not many people are inclined to learn this language, especially if they have no prior knowledge of programming languages. This is because, unlike Python, C++ is not very easy to learn. The codes are quite complex here, and it is definitely not recommended for beginners.
If you are new to the concept of programming languages, it is a good idea to learn simple languages like Python and R, practice writing codes in these languages, and then proceed to learn the basics of C++.
Why learning C++ can be useful for data scientists
In the recent past, many studies have been conducted to prove that learning C and C++ can definitely help data scientists. Here are some reasons why studying these languages can help you grow in your career as a data scientist.
- Yes, Python and R are used extensively in data science, but these programming languages are loosely based on C++ codes. To access the machine learning libraries of Python, you should know C++ codes.
- Currently, C++ is the only programming language that allows you to compile and process almost 1GB of data in one second! This enormous speed comes in quite handy when you deal with Big Data, advanced machine learning and computing projects. Its rapid processing speed definitely sets C++ apart from other languages.
- Though it is an old programing language, C++ has recently upped its ante with the introduction of new features and apps in it. The C99 standard, a new modification done in C++, has made this programming language compatible to be used along with two of the most popular data science programming languages today, Python and R.
- C++ aims to bridge the gap among all data science programming languages currently used, by creating its own libraries. These libraries can be accessed when you are using other programming languages too. Since more and more data science projects depend on machine learning and integrated libraries, it is only a matter of time before C++ will become as popular as Python in the field of data science.
- C++ codes form the basis for the code designed in other popular programming languages like Python. Since developers have used C++ codes to mimic the syntax of other languages, it becomes easier to modify these codes, as the base has already been strongly set up. If the developers had newly designed the codes for other languages from scratch, they would find it very difficult to modify codes, as they have no reference point to go back to.
As you can see, C++ offers multiple benefits to the field of data science. Data science involves dealing with huge volumes of data on a daily basis. Most data scientists are not sure why C++, with its extremely rapid processing abilities, is not being extensively used for Big Data and machine learning projects. Traditional data scientists, who have been in this field for many years, believe that learning C++ is definitely important in this career. The new breed, though, seems to have other thoughts. Many of them claim that they could do their job properly with only the knowledge of Python.
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To learn or not to learn?
Well, the answer to this one is going to be tough on you, if you are a data science beginner. Knowing Python and R can definitely help you kickstart your career in this field, but as you gain more experience, you would need to access the in-depth libraries of Python, and deal with complex, Big Data projects involving lots of computation and numerical analysis. C++ is the only programming language that would come to your rescue then, thanks to its extraordinary quick processing capabilities.
C++ back in trend
In the world of fashion, we might have noticed some old styles coming back to become the latest trends of today. The world of data science is no different. Though Python and R are steadily ruling the roost in the list of the most popular programming languages used for data science, C++, the old warhorse, is making a quiet but sure entry back into this world. Whether it will overtake the popularity of Python and its application is something we can only wait and watch. So, here’s our verdict – yes, C++ is needed for data science, and it would be a good idea for you to start learning the language as quickly as possible.
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