The Best Programming Language for Statistics

Every industry is seeing a significant increase in the demand for data scientists. Every business must evaluate the information it collects to grow. And data scientists need the right resources as well as the right skill set to gain a competitive edge with their data.

Data science is a platform that reflects statistics, data analysis, and related techniques to comprehend and interpret real-world phenomena using data. It incorporates theories and techniques from a variety of fields across the statistical, mathematical, computer science, and information technology disciplines.

Data science is becoming increasingly popular as machine learning is advancing. You must focus on learning one programming language to comprehend and become a data scientist, although learning multiple languages can also be beneficial in professional life, so you have several choices to choose from.

Now the question arises, which programming language is the best for statistics? 

Python and R are considered the best languages when it comes to statistics. Python has a large database where engineers and data scientists can ask for support and get answers to their questions. Whereas, R is a more specific approach and is mostly used to run statistical analysis. Both of them are open-source programming languages with great community support. 

Let’s learn more Python and R, along with the reasons why these are preferred among others in detail.

You should also check: Top 5 programming languages for Data Visualization

Programming Language for Statistics
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The Best Programming Language for Statistics

Python

Python has been around since the late 1980s, and it has evolved from harmony to consistency since then. This high-level programming language is now used in software development, mobile app development, web development, and the analysis and registration of statistical and scientific data.

Python was first used for automating repetitive processes, prototyping software, and deploying such applications through multiple languages. Because of the clean and simple code and extensive documentation, it is relatively easier to understand and absorb. Python is also more intriguing because it can be used in non-technical fields like industries and advertisements, making data analysis easier for experts.

Features

  • Since Python is a successively typed language, variables are automatically described.
  • As opposed to other programming languages, Python is more understandable and uses fewer codes to accomplish the same mission.
  • Python is a typed language. This eliminates the need for programmers to manually cast forms.
  • Python is an interpreted programming language. This means that the software was not required to comply.
  • Python is adaptable, easy, and capable of running on any device. It is resilient and can seamlessly connect with third-party applications.

R

It is a frequently used language. The R Foundation for Statistical Computing Supports R, which is an open-source language and software environment for statistical computing and graphics. Employers in machine learning and data science are looking for people with these skills.

Many mathematical models are available in R, and many analysts have written their applications in R. It is the pinnacle of open statistical analysis, and it places a strong emphasis on statistical models created with R.

Features

  • R is a free and open-source programming language. It is free to use and can be customized and adapted to the needs of the programmer and the project.
  • R has widened libraries that provide powerful visual capabilities, as well as the ability to produce static graphics with production-quality visualizations. This facilitates data visualization and interpretation.
  • CRAN, or Comprehensive R Archive Network, contains over 10,000 various formats and extensions that can be used to solve a variety of data science challenges.
  • R has a robust user interface, which means it can be used for both statistical computation and software development.
  • R is an interpreted language, which implies it does not require the use of a compiler to produce a program. R translates given code into lower-level calls and pre-compiled code automatically.

After Python and R there are some other programming languages too that are being used in statistics. But first: 

What is a programming language?

A programming language is a computer- and human-readable notational framework for representing computations. A programming language is a method for constructing executable models for a particular set of problems.

The programming language is a set of various instructions that take part in giving out the outputs. They are used to implement algorithms and in creating a software program. They provide a set of commands and syntax in creating the computer software.

Other Top Programming Languages for Statistics

The data science specialist is expected to be the most in-demand career in the coming decade, with the best technical minds continuing to create new methods for more effective data work. Down here, you’re going to be introduced to some other good programming languages and their features. It will help you understand what distinguishes them from each other.

They are as follows:

  1. Java
  2. SQL
  3. Julia
  4. Scala
  5. MATLAB
  6. TensorFlow
  1. JAVA

Java is a widely used particular programming language that operates on the Java Virtual Machine (JVM). This language is used by a large number of organizations, especially MNCs, to develop backend systems and computer applications. It’s a one-of-a-kind computing framework backed by Oracle that allows for platform functionality.

Java has been dubbed a cornerstone of the organization’s programming framework due to the increasing demand for Java skills. Java skills have seen a significant increase in demand, notably among software architects, software engineers, and DevOps engineers.

Features

  • Java is intended to be simple to understand. It should be simple to master if you grasp the fundamental concepts of OOP Java.
  • Java’s safe function allows the development of virus-free and tamper-proof systems. Public-key encryption is used in user authentication.
  • Unlike several other programming languages, such as C and C++, Java is assembled into platform-independent bytecode rather than platform-specific machine code. This byte code is transmitted over the internet and is processed by the Virtual Machine (JVM) on the computer on which it is operated.
  • Java focuses on compile-time error testing and runtime error detection to minimize glitch situations.
  • Java byte code is converted to machine language commands on the run and is not saved anywhere. Since linking is a gradual and light-weight operation, the creation process is more swift and analytical.
  • Since it is built to adapt to a changing world, Java is regarded as more dynamic than C or C++. Java programs can store a large amount of run-time data which can be used to validate and overcome object accesses in real-time.
  1. SQL

SQL (Structured Query Language) is one of the most common in the data science industry. It can be used well for verifying and updating the information contained in a relational database. Also, for decades, it has been primarily used for storing and processing data. It is used to handle especially big databases, and its quick processing time reduces the time it takes to respond to online applications. 

Since SQL is the most popular skill set for all organizations, having SQL skills might be the most valuable asset for machine learning and data science professionals.

Features

  • Client-server technological projects a many-to-one client-server partnership (one). In SQL, we have instructions that govern how a client application connects to a server over the internet.
  • SQL provides a framework to monitor the database, ensuring that only the database’s specific information is shown to the user and that the original database is protected by the database management system.
  • SQL has the capability of incorporating host languages such as C, COBOL, and Java to ask from them at runtime.
  • Transactions are an integral aspect of every database management system, and TCL is used to handle them. It includes commands like commit, rollback, and save point.
  1. Julia

Julia is a high-level dynamic programming language that was developed to meet the needs of high-performance statistical analysis, and scientific computing is quickly gaining traction among data scientists. It’s a more modern language that can also be used for particular programming which hasn’t been around for as long as R or Python.

Julia has been an excellent choice for dealing with complex tasks involving massive data sets due to its quicker execution. Many basic benchmarks run 30 times faster than Python and are often marginally faster than C code. Julia would be the next programming language to learn if you are into Python’s syntax and have a large amount of information.

Features

  • Julia is a programming language for scientific computation and data manipulation that is modern, descriptive, and high-performance.
  • Julia can manage several dispatches. The word “multiple dispatches” refers to the use of several different types of arguments to describe function behaviors. Julia generates code for various argument types in an optimal, specialized, and automated manner.
  • Julia’s shell-like features make it easy to control other functions of the system.
  • Julia has a large library of statistical operations of high numerical precision.
  • Although Julia’s community isn’t as developed as C++, Python, or R’s, the language’s adoption rate is rising.
  1. Scala

Scala (scalable language) is a well-known programming language with a wide user base. It is a user-friendly, general-purpose programming language that runs on the Java platform. Scala is a great language for working with massive data sets because it supports dynamic programming and has a good static-type system.

Scala is a great general-purpose language that is also a great choice for data science as it was designed to run on the JVM. This enables interoperability with Java itself.

Features

  • You don’t have to specify the data type or function return type directly in Scala. Scala is intelligent enough to figure out what kind of data it’s dealing with.
  • There are no fixed variables or methods in Scala. In Scala, a singleton object is a class that only has one object in the source file. The object keyword is used rather than the class keyword to identify a singleton object.
  • You can’t change its meaning because it’s immutable. You can also construct variables that are mutable and can be modified. Immutable data aids in concurrency control, which necessarily involves data management.
  • The actor model is included in the Scala standard library. You may use an actor to write concurrency code. Scala also contains Akka, a framework and method for dealing with concurrency. Actor-based concurrency is supported by Akka, an independent open-source application. Akka actors can be distributed or combined with transactional memory applications.
  1. MATLAB

This programming language was created and licensed by MathWorks. It is a statistical computing language that is fast, stable, and guarantees solid algorithms in research and business. Mathematicians and scientists associated with advanced computational needs such as Fourier transform, signal processing, image processing, and matrix algebra may find it useful.

MATLAB is a reasonable option for data science since it is commonly used in statistical analysis such as applications or day-to-day roles that involve complex, sophisticated computational features.

Features 

  • Data structures, control flow statements, functions, output/input, and object-oriented programming are all included in this high-level programming language. It enables the development of both fast throw-away programs and complete, complex, and wide application programs.
  • Iterative experimentation, design, and problem-solving are all possible with MATLAB’s interactive environment. It has features for managing workspace variables as well as importing and exporting data.
  • It includes built-in graphics for data analysis as well as tools for making custom plots. MATLAB provides high-level commands for rendering two- and three-dimensional data visualizations, animations, image processing, and graphical presentations.
  1. TensorFlow

TensorFlow is a great open-source numerical computing library. It’s a machine learning system that works well with large volumes of data. It is based on a fundamental principle. For example, if you’d like to run a graph of computations in Python, TensorFlow will run it using a collection of tuned C++ code that you create.

One of TensorFlow’s most important benefits is that the graph can be dissolved into several fragments that can run simultaneously on several GPUs or CPUs. Furthermore, it supports distributed computing, allowing you to train large neural networks on large training sets in a short period.

Features

  • One of the most important Tensorflow features is that it is versatile in its operation, which means that it has modularity and allows you to make parts of it individually.
  • Without a doubt, if it was built by Google, there is already a huge team of software engineers working on constant stability upgrades.
  • Tensorflow has attribute columns that act as intermediaries between raw data and estimators, allowing you to link your input data to your template.
  • Pipelining is a feature of TensorFlow that allows you to train several neural networks and GPUs at the same time, making the algorithms very effective on complex networks.

Conclusion

The field of data science is rapidly changing, and the number of methods used to derive value from data science is also growing. Learning any of the above-mentioned programming languages is a great way to get started in data science.

Though there is no particular order to this list of common data science languages, Python and R are competing for first place, but Python is still considered the best and most suitable programming language. As a data scientist, however, possessing multiple language skills gives you flexibility and professionalism.

Languages Best forLibrariesFeatures
PythonGeneral programming, data analysis and deep learning.Scikit-learn, NuPIC, Ramp Semantic way to generate complex plots.Allow to construct dynamic and interactive visualization.
RStatistical analysis and data analysis.Dplyr, Shiny, KnitrHelps consider data manipulation challenges.Provides a “verbs” function to help translate the thoughts into codes.
JavaScientific projectsMaven, Apache Commons, Guava.Consistent usage without any disruption.Works on multiple projects at the same time.
SQL Database manipulating, querying and updating.MySQL, SQlite, sqlite3High flexibilityEasy-to-useHighly secured
JuliaNumerical computing with a syntaxFlux, KnetDeep learning frameworksupports GPU operation and automatic differentiation.
Scala Support object-oriented programming and functional languages. Breeze, Saddle, ScalalabNavigate the graph of related entitiesCarry out client-side validation
MATLABArray manipulation and specialized math functionsloadlibrary, callibAllows the other modules to be loaded simultaneously
TensorFlowNumerical computation and large scale machine learning. Works with mathematical expressions efficiently.Deep neural networks and machine learning principles are well supported.

References

You can also consult these sites for more information:

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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