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Data scientists require statistical programming languages for a variety of reasons.

Using statistics analysis, linear algebra, probability, and calculus, data scientists need tools and coding languages to help them develop algorithms for ML.

Linear Regression, Logistic Regression, K Nearest Neighbours, K Means Clustering, and others are some of the most important algorithms.

Programming languages that are made with statistics capabilities, are necessary for anyone who wants to build these statistical models.

In this post, we’ll look at the differences between Macs and PCs, as well as the features that stand out as disadvantages or benefits of each operating system.

## What Is Statistical Programming?

**Statistical Programming refers to computation techniques that help in data analysis. **

Making sense of data by using statistical concepts/methodology is usually achieved by **writing a code**, and the programming language used to perform this task is called **statistical programming. **

This concept is widely used in many industries like Pharma, telecom, banking & finance, and also in weather forecasts.

Some languages come with statistical programming packages/libraries that offer a wide variety of statistical and graphical techniques to explore large data sets and create graphical displays of them for better and quick understanding.

These packages support statistical techniques like linear and nonlinear modeling, classification, clustering, time-series analysis, and others.

## What Are Examples of Statistical Programming Languages?

Data scientists use code to do statistical analysis, reconfiguration on unstructured data, and for predictive analysis.

**Python and R** are widely used languages for statistical analysis or machine learning projects. These are the best when it comes to statistic analysis.

But there are others – like **Java**, **Scala**, or **Matlab**. Both Python and R are state-of-the-art open-source programming languages with great community support.

With all these being said, here is our list of **the top 5 statistical programming LANGUAGES in demand in 2022:**

### R

R is a statistical computing language and graphics environment, created in 1992 by **statistician Ross Ihaka**, that is free to use.

It is a domain-specific language that aims to solve data analytics problems.

It builds and operates on a broad range of UNIX, Windows, and macOS systems.

R is extremely extensible and offers a wide range of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical) tools.

One of R’s advantages is how simple it is to create well-designed publication-quality graphs, complete with mathematical symbols and calculations when needed.

### PYTHON

Python is among the most popular data science programming languages of 2022. Python is great for programming because it is easy to learn, use and versatile.

It provides all the necessary tools for problem-solving — data collection & cleaning, data exploration, data modeling, and data visualization.

The closer you get to working in an **engineering** **environment**, the more likely it is you might prefer Python.

It’s a **flexible language** that is great to do something novel, and given its focus on readability and simplicity, its learning curve is relatively low.

You can use** Python libraries** to build data-driven web apps or if a statistics code needs to be incorporated into a production database.

Being a fully-fledged programming language, it’s a great tool to implement algorithms for production use.

Pros

- Popularity among data scientists.
- Endless support.
- A lot of resources.
- The syntax is very concise and easily readable.

Cons

- Relatively slow for computation in comparison to other languages.

### SQL

SQL is a must-learn query language that every data analyst or scientist needs to learn..

It is so important because a data scientist needs SQL in order to handle structured data and query databases. SQL allows you to access data directly and allow for statistics analysis, which makes it a very useful resource for data science.

SQL may be used to execute almost any function, including accessing data from a database, building a new database, and modifying data and databases, such as insertion, deletion, and updating.

SQL is a domain-specific language that is easy to use.

*People also use:*

### JAVA

Java is not a very popular language among data scientists, but it has some great ML and DS libraries: Weka, Jstats, Java-ML, MLlib, and Deeplearning4j.

Data analysis, machine learning, and data mining are all activities that Java can be used for.

Java allows you to create sophisticated applications from the ground up and provide findings much more quickly than previous languages.

Java also has a true garbage collection, it eliminates the need for the programmer to perform manual memory management which creates an added advantage over other languages.

The JVM ecosystem is a great reason for aspiring data scientists to learn Java because it provides an easy entry path to many more useful data science languages.

The downsides is that is not as flexible and friendly as other coding languages, it’s hard to learn, and support is less easy to come by, as Java developers are rarer and rarer to find.

### C/C++

C/C++ is a wonderful programming language for creating statistics and data tools, but it might be difficult to learn if you’ve never studied computer languages before.

This language comes in handy because it compiles data quickly.

The main benefit of C/ C++ is that it allows developers to delve deeper and fine-tune areas of the program that would otherwise be impossible.

Hence, developers with experience in low-level languages could use C/C++ for scalable projects.

C/C++ is extremely fast and is actually the only language that can compile over a gigabyte of data in less than a second.

The downside is that is on complicated side of programming languages for beginners due to its low-level nature.

### Worth Mentioned: MATLAB, SCALA

**FREQUENTLY ASKED QUESTIONS**

## Is Python A Statistical Programming Language

Python is a statistical language. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes.

## What is the best Statistical Programming Language?

We would consider Python to be the best Statistical Programming Language, majorly because of its versatility.

This language provides for the integration of SQL, TensorFlow, and a variety of additional data science and machine learning algorithms and libraries.

It is impossible to find an ideal language for data analysis because each language has its own set of advantages and disadvantages.

One language excels in visualization, while another excels at handling large data sets. The preference of the developer will also influence the decision.

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