A beginner data system developer can choose to focus on one of the programming languages, which will help master the difficult science of machine learning as quickly as possible. Nonetheless, it would be hard to advise which programming language is the best. The success of the developer in this area depends on various factors, so let's try to analyze them in detail in this article.
 
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Specificity

You have to be ready for the fact that as you deepen your specific knowledge in this area, you will have to reinvent the wheel on your own over and over again at each stage. Besides, you will have to find out how to correctly use all kinds of software packages and modules necessary for mastering the language you chose. How well and quickly you master it depends, first of all, on the availability of subject-oriented software packages for PL you chose.
 

Versatility

An experienced data specialist should possess excellent overall programming abilities, be able to carry out calculations and draw appropriate conclusions independently. Most of the regular work in the area of data science is aimed at finding, processing and adjusting the source data. Unfortunately, even the most advanced solutions for machine learning will not help solve the task in an automatic mode completely.
 

Efficiency

Commercial data science is developing rapidly in the modern world, offering new opportunities to get the expected result quickly. However, due to the rapid development of machine learning technologies, they are constantly accompanied by technical flaws, and your hard work can minimize them.
 

Performance

You are to improve the performance of the code you created quite often, especially processing massive arrays of data of particular concern. Important to note is that as a rule, compiled languages work much quicker than interpreted ones. Statically typed languages are much more fault-tolerant than dynamically typed ones. It appears that a decrease in productivity will be the only compromise.
Each of the programming languages presented below, to varying degrees, has one parameter in one or another group: versatility - specificity; performance - convenience.
 

The Most Sought-After Machine Programming Languages

Given these basic principles, let's find out more about some of the most famous languages used in machine learning. All the information below on the advantages and disadvantages of programming languages is based on the experience of highly qualified specialists.
 

Python

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The Python PL was introduced by Guido van Rossum in 1991. This language has become very popular with general-purpose and machine learning and is broadly used by specialists from all over the world lately. Python has a wide scope: it can be used in web development, game development, data analysis, and much more. The main working versions are currently represented by Python 3.6 and Python 2.7. Besides free license and numerous professional modules, developers of online services are attracted by a number of advantages:
  • The simplicity of learning the product. Python has a comfortable low entry threshold, and therefore it is an ideal programming language for beginners.
  • Using software like scikit-learn, pandas, and Tensorflow makes Python one of the most reliable machine learning applications.
Perfect software does not exist yet, so you need to be aware of Python’s flaws:
  • Like all dynamically typed languages, Python does not provide absolute type-safety. When working with Python, a developer should be especially careful as errors are often encountered with type mismatches. For example, when passing an argument (string) to a method that expects an integer.
  • When analyzing data or statistics, the R language is often faster and safer than Python.
Python is used by programmers who:
  • Want to understand data analysis quickly;
  • Are beginners to data analysis;
  • Work with statistical methods.
Conclusion: Python is a great option for machine learning at all levels - from beginner to advanced. Since the main task is to extract/convert/load data, Python is an optimal language in this regard. Programs such as Google's Tensorflow are worthy of a machine learning developer`s attention. The simple syntax allows you to write and debug code easily. It is easier for a Python developer to display information or visualize data on a site or in a web application.
 

R

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This programming language appeared in 1995; it was written in C and Fortran and represents a new generation of programming languages. This project, with a free license, has been one of the most popular for more than twenty years. The advantages of R:
  • An excellent set of high-quality open source object-oriented packages. R has at its disposal packages for almost any quantitative and statistical application that you can imagine. This covers neural networks, nonlinear regression, phylogenetics, the construction of complex diagrams, charts, and much more.
  • Besides the basic installation, we have the opportunity to install extensive built-in functions and methods. R also handles matrix algebra data perfectly.
  • The capability to visualize data is an important advantage, as is the ability to use various libraries, for example, ggplot2.
Advantages for software developers:
  • R allows working with large amounts of data;
  • Statistical models are written in several lines;
  • Work with complex calculations is greatly simplified.
Disadvantages:
  • Far from the highest performance. There is nothing to say in its defense: R is not fast.
  • Specificity. R is great for statistical research and data science, but not so versatile when it comes to programming for general purposes.
  • Other features. R has several unusual features that can confuse developers who are used to working with other languages: indexing starts with 1, the use of several assignment operators, and unusual data structures.
Conclusion: R is ideal for initial purposes. In the past, R was primarily used in scientific research, but it is rapidly expanding to the corporate market due to the growing popularity of BigData now. This is a powerful language that has a huge range of applications for collecting statistical data and data visualization, and this open-source language has many fans among developers. This programming language achieved wide popularity due to its effectiveness for the initial purposes.
 

C#

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The C# programming language has a lot of fans today, a large number of which are companies and start-ups creating indie games and 3D games. The benefits of this language include:
  • Support for the vast majority of Microsoft products.
  • Tools Visual Studio, Azure Cloud, Windows Server, Parallels Desktop for Mac Pro, and many others are free for small companies and individual developers.
  • A large amount of syntactic sugar, which is a special construct designed to understand and write code.
  • The C# language entry threshold is low. Its syntax has a lot in common with other programming languages, making the transition easier for programmers. The C# is considered the most understandable and suitable for beginners.
  • After purchasing Xamarin in C#, you can write programs and applications for operating systems such as iOS, Android, macOS, and Linux.
  • There is a whole community of experienced developers.
  • There are many job opportunities for C# developers nowadays.
Disadvantages:
  • Priority focus on the Windows platform;
  • The language is free only for small firms, individual programmers, startups, and students. The licensed version of this programming language will cost quite a sum for a large company;
  • The language left the possibility of using the unconditional jump operator.
Conclusion: the C# language is not particularly difficult for beginners, as it is easy to learn and understand.

 

Final Verdict

Python, C#, and R are not the only leaders in the large family of programming languages. Traditionally, you will find Java, JavaScript, C++, and a number of other programming languages on the top of various ratings. However, when choosing the optimal tool for machine learning for each specific project, it’s best to choose between Python, R and C#. Which one is the best is an incorrect question. The decision should be made depending on the tasks in each individual case.
 
R is becoming the most popular programming language for the implementation of the data analysis task, but some experts still turn to Python, since it is more convenient for manipulating data, displaying it, and analytics on web pages and applications, as well as working with repetitive tasks. If there is a need to create a tool for analytics at the initial stage of a project, it is better to choose R.
 
What about C#? This programming language is designed specifically for Microsoft products, so it is ideally integrated into Windows and absolutely all existing and upcoming Microsoft products. Certainly, C# will be in demand as long as Microsoft is relevant. If we consider the immediate approach, today’s 20-30 years old developer will have more than enough work until they grow old.