Do you believe it is worth it for young professionals gaining expertise in programming languages for Data Science in 2020? Absolutely yes, as the Data Science field is progressing, it is now among the most lucrative and in-demand professions. Over the last four years (since 2016), data scientists are among the top-paying jobs worldwide.
Thanks to the limited supply of professionals and the rising adoption of data sciences in various businesses, the demand for qualified data professionals worldwide is likely to increase in the coming years. In this changing job market, do you have the necessary data science language skills to advance your programming career? Read the article to find out more.
So, which are the 10 best programming languages for data science that are the most in-demand in 2020? Here is our recommended list:
These are some of the most popular programming languages used in data science. Let us take a detailed look at each one of them.
Rated among the best programming languages for data science, Python has been an open-source and user-friendly language since 1991. A November 2020 study shows that Python is ranked one among programming languages used in data science – with a share of 30.8% (up by 1.8% over the previous year).
Python is the preferred language for implementation in the Data Science domain thanks to its rich collection of data science libraries like Keras, TensorFlow, and Scikit-Learn. Add to that, Python is suitably designed for data-related tasks like data collection, modeling, and visualization. Python also has a growing community of data scientists and developers – that are open to queries and discussions.
Despite being among the oldest programming languages, Java is still the preferred language for enterprise-wide development. What makes Java among the top languages for data science? An extensive list of Java-enabled tools, including Hadoop, Scala, and Spark – is much-in-demand in the data science field. Add to that a complete list of tools and libraries in Machine learning and Data Science – that includes Weka, Java ML, and Deeplearning4j – and you can use Java to solve most of your data science problems.
What is more? Java Virtual Machines (or JVMs) are still the preferred choice for developers working on distributed systems, machine learning, and data analysis.
Other benefits that Java offers as among the most used programming languages for data science include user-friendly IDE environments for fast application development, seamless scaling of applications, and complex tasks involving data mining, data analysis, and much more.
Short for Scalable Language, Scala was developed in 2003 – as an extension of Java. What makes Scala among the top programming languages for data science is its in-built support for concurrency that can build efficient frameworks like Hadoop. Plus, it can be run on JVM and work with Apache Spark to handle siloed data.
Apart from being used in machine learning and web programming, Scala is ideal for handling Big data. Some of the other features of this programming language include over 17,500 in-built libraries, growing community support, and compatibility with various IDE tools including IntelliJ Idea, VS Code, and Atom.
When it is a matter of applications that require fast numerical analysis and high-level computations, Julia is among the top open-source programming languages to learn for data science – all thanks to its high speed and ease of use. As a multipurpose programming language, Julia is one language that can be used for low-level and high-level programmings – such as in matrices and algebra. This language also supports dynamic typing along with interactive use.
Additionally, Julia is used for front-end and back-end programming – and is packed with many functionalities such as support for parallel computing, an in-built package manager (with over 1900 packages), deep learning tools, and seamless interfacing with C, C++, R, and Python libraries.
R has been among the popular languages used for data science for it is a high-level programming language. Widely used for statistical applications with its open-source code, – R comes built with many libraries – like Dplyr and ggplot2 – that are used in data science applications. Some of the features of R that help in the statistical analysis include time series analysis, linear (and nonlinear) modeling, and clustering.
On the flip side, R is harder to learn than Python – however, third-party tools like RStudio and Jupyter do make it easier to create R applications. R offers good extensibility among its key features, making it easy for other programming languages to work with R’s data objects.
To be effective at your job in data science, you need to know how to convert raw data into valuable business insights. This is where you need to have a working knowledge of SQL (or Structured Query Language), which is a key component for data wrangling and extraction. Apart from connecting to your database, SQL enables you to derive useful facts and statistics from a large pool of Big Data.
SQL also helps in data pre-processing thanks to its seamless integration with most programming languages and database systems. Add to that, SQL has non-procedural functions that help you focus on the “What” rather than the “Why.”
Among the most fundamental programming languages in data science, C and C++ have been widely used to build various data science tools. An example of this is TensorFlow, whose core has been written using C++. In short, knowledge of these two programming languages can help you gain a better understanding and command over most data science applications.
Why is C/C++ still used to build tools in Data Science? On the one hand, C-based algorithms deliver faster and optimized results – and C language is still faster to execute than other programming languages. Data science frameworks built using C language can execute high-level code faster than other languages.
When it comes to applications that require high-level technical computing and computational mathematics, MATLAB is among the mandatory languages required for data science. Much before languages like R and Python became popular among data scientists, MATLAB – a matrix-based language – was widely used. Some of the typical applications of MATLAB include maths and computation, development of algorithms, data modeling & prototyping, and data visualization.
Among its primary functionalities, MATLAB provides native support for multiple file-formats, including images, sensors, videos, telemetry, and others. This language has advanced features like nonlinear optimization, financial modeling, system identification, control system designing along with machine learning and statistics. Add to that, MATLAB allows you to easily integrate its algorithms with third-party applications and other programming languages like C, Java, and .NET.
Introduced by Apple in 2014, Swift has gradually grown to become among the popular programming languages required for data science. Apple is also using Swift to develop iOS-based applications for its iPhone. Overall, Swift is rated more secure and efficient than Python when building mobile applications. Some of the popular iOS applications built using Swift include Lyft, Khan Academy, LinkedIn, and Clear.
Apart from Apple, Swift is also actively supported by Google and FastAI. What is more? Swift is like Python for its easy and readable syntax. As compared to Python, Swift is faster – thanks to its powerful LLVM compiler. Plus, you can easily import any C/C++ or Python library into Swift.
Through this article, we have discussed ten of the top programming languages for data science. However, the field of data science is expansive; hence you will need to learn more than a few languages to keep your career prospects high. Each of these discussed programming languages has its strengths and can be used depending on data-related requirements and application needs.
As an aspiring data professional, you can start learning any of the above languages to boost your career. Need more details? Take a look at all our 6-month online Full Stack Data Science Program, a unique course that helps you master all 3 Data Science elements – Statistics, Tools & Business Knowledge – with this complete hands-on & comprehensive program accredited by SSC NASSCOM!