While R and Python are becoming more and more common data languages, C and C++ can also be excellent options for successful and productive data research. However, how to choose which is better? Today, with this article we will try to settle the debate.
The programming languages C and C++ are both all-purpose. In actuality, C++ is a derivative of C, therefore they share many similar characteristics. Yet, because they started to diverge more and more over the past few years, C vs. C++ has become a popular topic today, especially among aspiring data scientists.
How are they different?
C++ is used to handle complicated jobs that C was unable to handle. For instance, C++ allows for more programming styles than C, and has a stronger type-checking system. Since C does not provide exceptions, it is also simpler to find bugs and other problems in C++ code. Problems that develop when the program is running are referred to as exceptions. The exception-handling function in C++ provides a means to isolate and review a section of code.
Since C is an easy language to learn, mastering these ideas is easier for beginners. You can, however, start learning C++ straight away and omit learning C. Since C++ programming habits are very different from those of C, making this choice now might prove beneficial in the future. The speed of C++ versus C should not be given too much consideration. Both languages are performance-oriented, and they rarely deviate from one another. For instance, in some circumstances, well-written C++ codes can even be faster than C.
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C vs. C++
Some key differences between these two programming languages are:
- C++ is an object-oriented language, whereas C is a procedural language. This characteristic alludes to the programming approach that programmers use. Procedural programming, for instance, adheres to the sequential requirements of functions, but object-oriented programming is more concerned with objects, inheritance, etc.
- The exception handling in C++ is carefully thought out (Try and Catch sections), which makes debugging simpler than in C. Finding challenging faults makes good use of this feature.
- In addition, C++ permits information concealing (closely related to encapsulation). Since C++ has modifiers that can restrict user access, data is safer in C++ than in C.
- As C++ allows for function overloading, the same function can be specified with distinct meanings.
- Namespaces are another feature of C++ that enables you to organize codes according to the desired scope. For instance, grouped entities may be placed in a namespace scope, which is a more restricted scope. This feature is not supported by C.
- Experts connect the term “multi-paradigm” with C++. Despite the fact that C++ is an object-oriented language, it nonetheless incorporates procedural features. Since C only adheres to procedural logic, C++ is more versatile than C.
The comparison of the syntax rules for C and C++ yields the following key findings:
- Due to how differently C and C++ are used, distinct approaches to programming will be used.
- Developers can use procedural and object-oriented programming techniques with C++.
- Only procedural programming is supported in C.
- Additional features are available in C++, including information hiding, scope management, data security, and error handling.
- For novices, learning the fundamentals of low-level programming in the C language could be easier and more beneficial.
Which performs better?
Performance comparisons between C and C++ typically show that C is quicker than C++. However, C++ does win this race in some circumstances. Certainly, compiled languages will always be quicker than interpreted languages. It can be challenging to compare the two compiled languages’ speeds, though.
The speed of the created code has a significant impact on C vs. C++. The performance of well-written C++ codes can be on par with or better than well-written C codes. For instance, C++ is likely to perform robust programming more quickly than C. As a result, experts do not claim that one language is quicker than the other. When comparing the performance of C with C++, it is frequently said that C++ can compete with C.
Although C++ does include a number of features that could potentially result in subpar performance, their use is not required. Even if you do, the speed disparity made possible by contemporary compilers, most likely, won’t be enough to favor one programming language.
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Which should you learn?
Since it has more features than C, learning C++ first would seem like the wisest course of action. However, because of how simple this language is, C aids in your understanding of how hardware, particularly CPUs, operates.
You will become acquainted with the idea of low-level programming and discover pointers and appropriate memory allocation. In addition, there are fewer features in C that beginners will need to examine. The manner you wish to work will, however, determine whether you should learn C or C++ first. Before learning C++, there is no real need to learn C. Even if you begin by studying C++, you will likely run into some of the same syntax and semantic components.
Developers who begin with C frequently gripe that they must abandon the habits they developed in C in order to utilize C++ appropriately. Studying C first might prevent developers from exploring the object-oriented side and force them to continue with procedural programming. In summary, there is no clear winner in the C vs. C++ contest because each language has its own advantages and disadvantages. However, keep in mind that C is a procedural language that adheres to a top-down programming approach.
Object-oriented C++, on the other hand, is more concerned with objects and features like inheritance and information hiding. Moreover, C++ enables programmers to adhere to some procedural programming principles. In terms of the learning strategy, it could be preferable to study C first because it will make machine code, memory allocation, pointers, and other concepts easier to understand for beginners.
Irrespective of what you want to learn first, it would be a good idea to think about the nature of the data science job you plan to do, and then study that particular language that you are likely to use more frequently.
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