I am Gajesh Naik. I am 12 years old. I started programming at the age of 7. I have skilled 9 P. Lang
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.
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
Also check my blog on Python Libraries for Data – https://techwithgajesh.com/top-9-python-libraries-for-data-scientists/
Python for Data Science — Course for Beginners
COVID-19 Analysis using Python
– Eric Freeman
- 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
Github link: Learn more about D3.js
Github link: Learn more about TensorFlow.js
Github link: Learn more about Brain.js
Github link: Learn more about Machinelearn.js
Github link: Learn more about Math.js
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
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
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.
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
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
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
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
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
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.
More Programming Languages for Data Science —
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.
Note: To eliminate problems of different kinds, I want to alert you to the fact this article represents just my personal opinion I want to share, and you possess every right to disagree with it.
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