Introduction

The wonderful world of the Data Science domain usually resides in high-level, declarative programming languages. A prime example of such a language is Python, but just looking at the list of most popular languages quickly reveals what kind of syntax most Data Scientists prefer to work with. The biggest three languages that I usually attribute to Data Science are Python, R, and Julia. While Scala, SAS, and similar solutions are certainly noteworthy, especially with some of those options being more popular than the Julia language. However, I figured Julia deserved the spot because of its rapid increase in adoption. The point here is to examine the properties of those languages.

Are is a multi-paradigm, declarative, interpreted programming language that was created for statistical analysis. Python is a general purpose multi-paradigm, declarative interpreted language. Julia is also a multi-paradigm, declarative, programming language, but it is compiled. What we can consider here is the striking similarities between these languages. All of these languages have pretty simple syntax that is easy to learn. Since all of the most popular languages used for Data Science today share this same attribute, it is safe to assume that this is a preference for Data Scientists.

Despite this preference, however, there are still certainly applications in Data Science for other programming languages. For example, C++ has become quite popular with machine-learning engineers. These sorts of languages have a number of advantages compared to the typical interpreted programming languages that are used for Data Science. While often it can be said that if you want to get into Data Science you should learn Python or R, which is certainly true, there are also some benefits to knowing languages like C or C++.

In particular, I have found that the C programming language has really come in handy for Data Science work. Today I wanted to discuss the reasons why this is the case, provide a little synopsis on what is great about C, and explain why I think that Data Scientists might want to pick up C. Of course, the answer to whether or not C is a good choice for you is always going to depend on what you want to actually do, but I think in most applications C can be a valuable asset towards getting Data Science done.

General purpose reasons to learn C

Before we get into the reasons that Data Scientists might want to learn C, let us first go over the reasons that you might want to learn C regardless of a Data Science attribution. There are many cases where Data Science can turn into general-purpose engineering, and I think that knowing C as a general-purpose language first and a Data Science language second is certainly a wise choice.

Versatile

The first great thing about the C language is that it is very versatile. Most libraries and headers are written in C. Nearly every programming language written can interpret C, because without working with C the language loses access to the code that has been built to run entire systems. The kernel that you are sitting on top of to read this article was most likely built in C.

Everything, from the lowest level of system input all the way to the highest level of web-development can be done in C. Along with this, C is a venerable language that has been used for a very long time. There are a lot of libraries available for the C language.

Learning

As a Data Scientist, one of my top priorities is to always learn, no matter what I am doing. Learning is a very important aspect of Data Science because there are so many different disciplines that Data Science involves. That being said, you can spend the rest of your life studying just one of these disciplines. If you want to learn more about computers, how they work, and how they interact with code from a more low-level perspective, C is a great choice to do so with. When I learned C, I learned so much about computers, and that information has come in handy when handling just about any problem, even in languages like Python.

Fast

As you might expect from a language like C, it is a very fast language to compile. This is especially true compared to the options we typically use for Data Science. If you need your code to run faster, you can always drop down to C in order to make it so. Furthermore, C code can be optimized more and more because it allows you to interact more directly with the hardware on your computer.

C For Data Science

Now that we better understand why C is great at doing what it was meant to do, let us now consider why it would be a great choice or a Data Scientist to learn. One thing I do want to consider first is that the C programming language is definitely not what one should use to get into Data Science. There are so many other options that can get the job done effectively while providing a lot less up-time when it comes to learning. This is even more the case when you consider just how little it is used in some Data Science jobs.

Python

The Python programming language has become extremely popular among Data Scientists. This language is written in C, and ultimately gets interpreted by C. That being said, we can easily interact with Python using C with the Python.h header. Needless to say, this can come in handy when one wants to make more optimized code for Python. Most of the packages that are typically used for Data Science in Python actually take advantage of this. Consider that Pandas and NumPy, for example, are both at least partially written in C.

Fast (again)

Machine learning can be intensive on processors. While many solutions have moved to a parallel computing platform, sometimes it might be a better idea to start with more iterative code instead. C is a really fast language, and it can be a lot easier to optimize, this can lead to faster algorithms, so it is certainly a great choice for implementing machine-learning algorithms that could take a lot of processing or memory to perform.

C++

A language that has proven to be quite popular with machine-learning enthusiasts is C++. In a lot of ways, the ++ in C++ is only object-oriented programming, and a few useful features to make writing C easier. That being said, learning C can be a very solid stepping stone into learning C++. The code for both programming languages often turns out to be very similar, and it is easy to see why a Data Scientist might want to know C++. If you want to be a machine-learning expert, and zero in on that portion of Data Science, then C++ is a great choice. This is especially true for finding jobs, as C++ is quite a popular choice for low-level machine-learning engineers.

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