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Why use C++ for your data science business applications?

  • Data scientists are considering programming languages as they start exploring new roads for big data development. C++ software development keeps popping up in the data science space as it’s comparably simple and robust at the same time

C++ for your Data Science business applications

As more businesses showed the need for big data applications, developers are exploring new programming languages that could fulfill their needs. Some programming languages have been particularly accepted for data science, but this is something that is beginning to change. The number of data science applications on the market has grown exponentially over the last few years. However, the growth of these new applications fails to keep up with the market. This is the reason for the company's peaked interest in C++ WebSocket server options. 

Python has generally been the preferred programming language for data scientists. According to one poll, it is found that 66% of data scientists used Python to create their business application development.  R had been a more popular programming language for data science, but Python became more appealing for various reasons. While languages like Python and R are increasingly popular for data science, C++ can be a strong choice for efficient and effective data science. In this article, we are going to explain how, but before that, let’s see what data science is.

You may also read: Python Vs C++ Programming

What is Data Science?

Data science is a mixture of several tools, algorithms, and machine learning principles to identify hidden patterns from the raw data. But what separates it from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting. 

 Data analyst Vs data Scientist


As you can observe from the diagram, a
Data Analyst usually explains what is going on by processing the history of the data. Data processing is a process of turning raw data into computer-understandable formats. On the other hand, Data Scientist not only performs exploratory analysis to see insights from it but also uses different advanced machine learning algorithms to identify the occurrence of a particular process in the future. Traditionally, the data that we have is mostly structured and small in size, which could be interpreted by using simple BI tools. Unlike data in the conventional systems, which was mostly structured, today,  most of the data is unstructured or semi-structured. 

A Data Scientist will analyze the data from every angle, sometimes angles not known earlier. So data science applications are essentially used to make decisions and predictions using predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.

You may also read: C++ developer for IoT application projects

C++ for Data Science applications

Data scientists are considering various types of programming languages as they start exploring new roads for big data development. C++ software development keeps popping up in the data science space as it’s comparably simple, but the language is robust at the same time. When you need to compute huge data sets instantly and your algorithm isn't predefined, C++ comes in handy. Whenever C++ is used, pointers need to be used correctly and header files need to be complete. For dynamic load balancing or a highly efficient adaptive caching layer, C++ will be the best language to implement. There are several reasons why C++ is becoming more appealing for data scientists. Some of these reasons are outlined below.

C++ has very rapid processing capabilities

When it comes to developing data science applications, the speed of the compiler is one of the most important characteristics. Therefore, it is rather unusual that C++ has been neglected as an excellent data science programming language. Considering the fact that C++ is the only programming language that can compile over a gigabyte of data in less than a second. As you can compile comprehensive data sets with C++ a lot more quickly, it is a great language for large, data-driven projects.

C++ support System Programming

Google’s MapReduce was written in C++ at first, and then it was rewritten in Java.  Moreover, MongoDB was also written in C++. In the case of deep learning and deep neural networks,  C++ is one of a few languages that can be used to write deep learning algorithms. This is because data scientists are very fond of C++ and Python libraries. Most of the deep learning algorithms require implementations in C++. For instance, Caffe is a successful deep learning algorithm repository and machine learning software.

Modifying code for other languages is easy

Most modern programming languages are based on C or C++. Being platform-independent, the syntax of C++ is relatively similar across most platforms. There are usually a lot of common features between C++ and other object-oriented programming languages.  Developers that are trying to replicate the code with another language, such as Python, will need to make fewer adjustments than they would if they used almost any other object-oriented programming language as a starting point.

Resource Consumption and Cost

Desktop applications in C++ usually need less capacity and electric power than virtual machine languagesThis helps to lessen CapEx and OpEx as well as server farm costs. In the matter of costs, C++ also reduces the total cost of development. When we talk about resource management, C++ provides features that other languages require. Further, you also have access to unrestricted templates to write generic code.

Developing data science libraries for other languages

People outside the domain of computer science think that programming languages are a lot more fragmented than they actually are. It is also believed that there is no interconnection between various languages, which is not true at all. One of the biggest connections between different programming languages is their libraries. C++ is a notably effective programming language for developing new libraries, which can be used across other programming languages. Since data science applications are very reliant on new programming libraries, the future of C++ programming and development can play an important role in this aspect.

C++ and Data science

There are a lot of exceptional reasons to consider using C and C++ for data science and analytics applications. This can be great for processing large data sets very quickly, which is going to be very beneficial. It can also be very useful for developing new libraries that will be used in other programming languages for better data science solutions. C++ is the language that is used in a lot of places but largely in systems programming and embedded systems. By system programming, we mean developing the operating systems or drivers that communicate with hardware, and the embedded systems are automobiles, robotics, and appliances. On top of that, it has a big and rich community and developers, which makes the process to hire C++ developers and obtain online solutions easy.

Evon Technologies has been one of the C++ development companies in India since 2006. Our C++ application development services include native C++ application development, cross-platform C++ application development, custom C++ application development, upgrading/porting the existing application, and C++ application support and maintenance. We have worked with C++ over the years with our global clients and we strongly believe in the language’s power and capabilities. Thus, we encourage and promote using C++ for developing your next business application. Reach us here or email your concerns at This email address is being protected from spambots. You need JavaScript enabled to view it..

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