Top Programming Languages for Data Science in 2020

Which programming language should you pick up to start your data science journey?

“The only way to learn a new programming language is by writing programs in it.” — Dennis Ritchie

A programming language is the superpower of any developer. Every once in a while, a new programming language or an update to an existing language pops up that tries to deliver faster and more optimized results. Developers can often find themselves entangled with a wide variety of programming languages, deciding which one to choose for their next project.

All programming languages are not similar and in many instances, what works for one project or a requirement might not work for another. Data Science is one such trending domains where the demand for efficiency and high-performance results are skyrocketing. This article will cover a handful of those top-tier programming languages.

What You Need to Consider When Choosing the Best Programming Language for Your Data Science Career Path

Now before we jump to the section covering some of the top programming languages for Data Science, mentioned below are a few of the questions that you should ask yourself:

● What exactly is the task at hand?

● In what way can Data Science help you with it?

● How skilled are you in the programming languages that you already know?

● Are you prepared to take your knowledge to the next level?

● At what scale does your organization use Data Science?

● Are you interested in learning advanced Data Science?

Top Programming Languages for Data Science in 2020

Now that you have answered the questions above, let’s move on to the next section. From here on, we would like to draw your attention to some of the most used programming languages for Data Science. You might already be familiar with a few of the popular programming languages, while some may be completely new for you.

1. 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

2. JavaScript

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

3. Java

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

4. R

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

Conclusion

Throughout the article, we covered some of the top programming languages for Data Science. Each of these languages come with their benefits, often offering better and faster results when compared with others. The domain of Data Science is exceedingly vast and can often demand a different set of tools for various tasks.

Equipping yourself with more than one programming language can guarantee to help you overcome unique challenges while dealing with the data. If you are a budding Data Scientist, you should start with the programming languages mentioned above as they are the most in-demand languages right now.