Top Programming Languages for Data Science.

1. Python

Source — Python

Python holds a vital place among the top tools for Data Science and is often the go-to choice for a range of tasks for domains such as Machine Learning, Deep Learning, Artificial Intelligence, and more. It is object-oriented, easy to use and extremely developer-friendly thanks to its high code readability.

Python’s vast ecosystem of rich libraries and implementation for various purposes makes it a genuinely multi-faceted option. Some other key standout features offered by Python include:

● Support for powerful Data Science libraries such as Keras, Scikit-Learn, matplotlib, TensorFlow and more

● Perfectly suited for tasks like data collection, analysis, modeling, and visualization

● Supports numerous file export and sharing options

● Comes with a strong community for getting support

The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code — not in reams of trivial code that bores the reader to death.

- Guido van Rossum

PYPL Popularity of Programming Language —

2. JavaScript

Source — JavaScript

JavaScript: Don’t judge me by my bad parts, learn the good stuff and stick with that!

- Eric Freeman

The multi-paradigm and event-driven scripting language JavaScript is among the top programming languages for web development. With JavaScript, developers can create rich and interactive web-pages, and it is this property of JavaScript that makes it an amazing choice for creating beautiful visualizations.

Other uses of JavaScript for Data Science include managing asynchronous tasks and handling of real-time data. A handful of compelling reasons in favor of JavaScript are:

● Allows to create visualizations for data analysis

● Supports various modern-day Machine Learning libraries like TensorFlow.js, Keras.js, and ConvNetJs, to name a few

● Is easier to learn and use

Top JavaScript Libraries for Data Science

• D3.js:

Github link: Learn more about D3.js

• Tensorflow.js:

Github link: Learn more about TensorFlow.js

• Brain.js

Github link: Learn more about Brain.js

• Machinelearn.js

Github link: Learn more about Machinelearn.js

• Math.js:

Github link: Learn more about Math.js

JavaScript Language over time —

JavaScript is the most popular language according to GitHub repositories contributions

Source: Octoverse

3. Java

Source — Java

Write Once, Run anywhere

The programming language Java might look old, but don’t let that fool you. It has been long used by some of the top businesses for secure enterprise development as their preferred development stack of choice. To cater to the boom in the Data Science space, Java has offered tools such as Hadoop, Spark, Hive, Scala, and Fink.

Java Virtual Machines are a popular choice for developers to write code for distributed systems, data analysis, and machine learning in an enterprise environment. Other key benefits offered by Java include:

● Offers several IDEs for rapid application development

● Is used for tasks involving data analysis, Deep Learning, Natural Language Processing, data mining and much more

● Enables effortless scaling to build complex applications from scratch

● Able to deliver results faster

If Java had true garbage collection, most programs would delete themselves upon execution.
— Robert Sewell

4. R

Source — R Foundation

R is an open-source software environment primarily for handling the statistical and graphics side of things in Data Science. Time series analysis, clustering, statistical tests, linear and non-linear modeling are just some of the many statistical computing and analysis options provided by R.

Third-party interfaces like RStudio and Jupyter make it easier to work with R. R provides excellent extensibility, often allowing other programming languages to modify data objects in R without much hassle, thanks to its strong object-oriented nature. The key takeaways from the programming language R are:

● Offers efficient handling of data and additional data analysis tools

● Provides a great many options for creating excellent plots for data analysis

● Allows extending the core functionality with robust community-built packages

● Includes an active community of contributors

5. C/C++

C is one of the earliest programming languages, and most newer languages use C/C++ as their codebase, one such example would be R. Working with C/C++ requires a strong understanding of the fundamentals of programming.

Even though C/C++ is among the more complicated side of programming languages for Data Science beginners due to its low-level nature, it is increasingly being used to build tools that you can use for Data Science.

Take TensorFlow, for example, its core is written in C++, while the rest of it is in Python. But that’s not all, C does have a couple of strong points, mentioned below:

● Ability to deliver faster and better-optimized results when the underlying algorithms are also written in C

● Comparatively faster than other programming languages due to its efficient nature

Google Trends — C/C++ Interest Over Time

Google trends C/C++(source)

6. SQL

Source — SQL

Being a programmer, I’m sure you must’ve used SQL at some point in your life. SQL doesn’t merely connect you to your database, it serves a very crucial purpose and that is, it gives you the facts and statistics from a vast pool of data, with just a few queries.

Some of the features that increase the importance of SQL for simplifying the various tasks in Data Science, such as data preprocessing, are:

● The non-procedural nature of SQL lets you focus on the What, instead of the Why

● Integrates well with programming languages and database management systems alike

● Helps you connect to your data to understand it better

● Allows smoother management of huge amounts of data

According to Stackoverflow, Most Popular Technology —

Source — Stackoverflow Survey

7. MATLAB

Source: MathWorks Logo

MATLAB is primarily a mathematical computing environment designed for performing advanced numerical computations and comes with various tools that can help you carry out operations such as matrix manipulation, data and function plotting, and much more.

With MATLAB, you can tackle the trickiest of the mathematical and statistical problems with ease. It is widely used in academia for teaching linear algebra and numerical analysis. Key takeaways from MATLAB include:

● Allows implementation of algorithms and user interface creation

● Comes with a powerful collection of mathematical functions

● Offers built-in graphics for creating custom data plots and visualization

● Enables seamless scalability

8. Scala

Source: The Scala Programming Language

Scala is a high-level programming language that runs on the Java Virtual Machine and can make working with Java easier. Scala can be used effectively with Spark to handle large amounts of siloed data. The underlying concurrency support makes Scala a perfect choice for building high-performance Data Science frameworks, such as Hadoop. Key offerings by Scala include:

● Is stable, versatile, and can deliver results comparatively faster under certain situations

● Comes with over 175000 libraries extending Scala’s functionality

● Is supported on various IDEs, such as IntelliJ IDEA, VS Code, Vim, Atom, Sublime Text, and even in your browser

● Offers strong community support

9. Julia

Source — Julialang.org

A fresh approach to technical computing

Julia is a dynamically-typed multi-purpose programming language but makes for a suitable choice for numerical analysis and computational scientific analysis. Although a high-level programming language, Julia can also be used as low-level programming, if needed.

Julia has been using by some high-profile businesses for a variety of tasks, including time-series analysis, risk analysis, even space mission planning. Other notable features of Julia include:

● Focus on delivering high-performance

● Built-in support for a package manager

● Offers data visualization, operations on multidimensional datasets, and robust tools for Deep Learning

● Support for parallel and distributed computing

10. SAS

Short for Statistical Analytical System, SAS is an industrial-grade software environment built specifically for business intelligence, predictive analysis, and advanced analysis.

SAS also allows the users to mine, alter, and manage data from a variety of sources for the sole purpose of advanced statistical analysis.

The software environment is broken down into sets of tools that offer the mentioned functionality. Some of these cover the presentations, some cover data management, some cover quality control, and a handful more for features such as code editor and project manager, grid computing manager, and so on.