Although not the first choice for many, C++ finds an irreplaceable spot in any data scientist’s toolkit. Despite the fact that we have a number of tools and frameworks just for managing big data and working on data science, it is important to note that on top of all these modern frameworks is a layer of a low-level programming language - the likes of C++. The low-level languages are responsible for actually executing the high-level code fed to the framework.
The reason for C++ being such an indispensable tool is that not only is it simple, but also extremely powerful, and is one of the fastest languages out there. Being a low-level language, C++ allows data scientists to have a much broader command on their applications. It allows them to dig deep and fine-tune certain aspects of the application which wouldn’t have been possible otherwise.
Further, a well-written program in C++ will have an intricate knowledge and understanding of the architecture of the machine along with memory access patterns and can run quite faster than programs written in languages that rely on a garbage collector for memory management issues.
For these reasons and more, many enterprise developers and data scientists with massive scalability and performance requirements tend to be inclined towards the good old C++. Many organizations that use Python or other high-level languages for the data analysis and exploratory tasks still rely on C++ to develop programs that feed that data to the customers - in real-time.
The work scope of most of the data scientists revolves around building machine learning products. These products generally have major real-time latency constraints. Hence, the high-level languages, having a garbage collector for memory management, don’t fit the bill, and the data scientists turn to C++. Building and deploying everything in C++ provides a fair amount of latency guarantees.
If you’ve been paying attention till now, you’d have realized the incomparable importance that C++ holds when it comes to big data and data science. Now, let’s talk a bit about how C++ skills help you in your data science career.
When we talk about complex machine learning algorithms and applications, we are essentially talking about extremely large sets of data (in terabytes or petabytes, if not less) that need to be processed quickly - as quickly as real-time. Further, more often than not, you’ll need to use cloud computing, parallel processing, or cluster computing techniques.
C++ is the only language that is capable of processing 1GB+ data in under a second. To add to this, it allows you to retrain your model and apply predictive analytics in real-time, and even the system record’s consistency. These reasons make C++ a preferred choice for you if you’re a data scientist looking to speed up the processing time of your applications.
Before being written in Java, Google’s pathbreaking MapReduce was written in C++. Further, the most commonly used NoSQL database for big data management - MongoDB - is also developed using C++. So, if as a data scientist, you’re required to write system programs, C++ would be the first language you’ll turn to.
When it comes to deep learning using deep neural networks, C++ is one of the few languages that can be used to train these networks efficiently. Most of the deep learning algorithms used today are implemented using C++. For instance, one of the most preferred deep learning algorithm repositories - Caffe - is coded in C++ with Python and Matlab bindings.
Typically, C and C++ applications require extremely less electric power, memory, and space than high-level virtual machine languages. This helps in reducing capital expenditure, operational expenditure, and even server farm costs. It is evident from here that C++ significantly reduces the total cost of development.
Talking about resource management, C++ provides many features that other languages lack. Further, the language also gives you access to extensive templates that allow you to write generic codes.
Like we discussed earlier, C++, being a low-level language, allows you to fine-tune the performance of the application in ways that aren’t possible if you use high-level languages for the same. C++ offers the developers with a much better control of the system memory and resources.
The common notion is that R and Python are faster, but that is far from the truth. A well optimized C++ code could run hundreds of times faster than the same piece of code written in Python or R. The only challenge with C++ is the amount of work you need to do to get the readymade functions to work. You need to know how to deploy and manage pointers - which can honestly be a bit complicated. But the knowledge of working with pointers or managing the code in C++ comes at a beautiful cost - that of knowing the intricacies of your application or code. A deep insight into the inner workings of your application allows you to better debug your application in case of errors, and even build features that require micro-level control of your system.
While the knowledge of C++ isn’t essential for an aspiring data scientist, it definitely goes a long way to help you find a solution to problems where all other languages fail. There are numerous data science courses that help you understand the intricacies of the domain and teach you how to work with the most commonly used tools and libraries. However, it is still recommended to do your due diligence and get acquainted with, if not master, the skills of programming using C++. It’ll help you be the data scientist whom everybody turns to when none of the high-level languages seem to be solving the problem at hand!
Have you worked on C++? What other benefits do you think it offers other than the ones we discussed? Do let us know in the comments below!