If you are considering a career in data science, you must first master different data science programming languages.
Data science is a field of study combining mathematics, statistics, programming skills, and domain expertise to draw meaningful insights from large volumes of data. Data scientists use machine learning algorithms to produce artificial intelligence (AI) systems capable of performing tasks that ordinarily require human intelligence.
Data science spans multiple disciplines and uses various tools, libraries, and programming languages to extract value from data. Since programming is one of the essential skills for a data scientist, data science programming languages are worth exploring. However, getting started with coding can seem daunting, especially if you have no prior experience.
This article will look at some of the best programming language for data science and highlight their strengths.
Best Programming Language for Data Science
Here are the top 10 best language for data science to help you ace your data science career.
Python is an open-source, object-oriented, general-purpose programming language with applications in data science, web development, video game development, and other domains. Ranking #1 in PYPL and #2 in the TIOBE index, Python has a simple and easy-to-learn syntax, built-in high-level data structures, and dynamic typing and binding.
Moreover, Python’s rich ecosystem of libraries, powerful packages, and robust community support makes it ideal for data science operations, from data preprocessing and statistical analysis to visualization and deployment of AI and ML models. Some extensively used Python libraries for data science and machine learning include pandas, NumPy, sci-kit-learn, Matplotlib, Keras, and TensorFlow.
R is emerging as one of the go-to data science programming languages, with a #7 rank in the PYPL index. R is an easy programming language exclusively designed for data science. It is an open-source, domain-specific language and environment for statistical computing and graphics. R is highly extensible and offers expansive statistical and graphical techniques, including classical statistical tests, linear and non-linear modeling, classification, and time series analysis, to name a few.
One of the perks of using R is that you can easily create well-designed publication-quality plots with mathematical formulae and symbols. R compiles and runs on UNIX, Windows, and macOS systems.
Ranked #3 in the TIOBE index and #2 in the PYPL index, Java is a general-purpose, object-oriented programming language used for data mining, data analysis, machine learning, developing embedded systems, etc. The Java ecosystem is known for its efficiency, performance, and ability to build complex applications from the ground up. However, recent years have seen the popular programming language make a mark in data science.
Thanks to the Java Virtual Machine (JVM), Java provides an efficient and robust framework for popular big data tools such as Spark, Hadoop, and Scala. The high-performance capabilities of the language are ideally suited for performing data operations that demand complex processing requirements and massive storage.
The lightweight, interpreted programming language supports popular machine learning and deep learning libraries such as Keras and TensorFlow and visualization tools like D3. JS’ widespread popularity in the web developer community makes it an excellent medium for front and back-end programmers looking to explore different aspects of data science.
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C is a machine-dependent, procedural programming language, and its close relative, C++, is an object-oriented programming language. Although both the languages have similar syntax and code structures, C++ is a superset of C with features like exception handling and a rich library. Moreover, C is one of the earliest programming languages, with most modern languages using C/C++ as their codebase. C and C++ are beneficial for data science applications due to their ability for quick data compilation. The low-level natures of C and C++ facilitate easy app customization that otherwise would not have been possible. C/C++ is best for projects with massive performance and scalability requirements.
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Structured Query Language (SQL) is a domain-specific language for retrieving and managing data in a relational database. All relational database management systems (RDMS) such as MS Access, MySQL, Sybase, Oracle, SQL Server, and PostgreSQL use SQL as their standard database language. Although these relational databases have subtle differences, their basic query syntax is quite similar, making SQL a versatile option.
Thus, database query requires sound knowledge of SQL, and since SQL gives access to data and statistics, it is a vital resource for data scientists. Moreover, SQL has simple, declarative syntax, making it relatively easy to learn compared to other languages.
Scala is ideal for handling large-volume data sets, making it suitable for big data and machine learning. When used with Spark, Scala can handle large amounts of siloed data. Besides, Scala is perfect for building high-performance data science frameworks like Hadoop.
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Released in 2011, Julia is one of the youngest data science languages on this list and one with rising popularity. Julia is a dynamic, high-level programming language with the ease of Python and the speed of C/C++. Great for numerical analysis and scientific computing, some of Julia’s earliest applications were in biology, chemistry, and machine learning. Although Julia is a general-purpose programming language for game development, web development, and the like, it is widely considered the next-generation language for data science and machine learning. It is a versatile programming language supporting parallel and distributed computing with the ability to function as a low-level programming language when needed.
Like Julia, MATLAB is a fourth-generation, high-level programming language for numerical computing. Initially used in academia and scientific research, MATLAB provides robust mathematical and statistical operations tools, ideal for data science applications. MATLAB allows users to plot functions and data, perform matrix manipulations, analyze data, implement algorithms, create models, etc. However, one of the significant downsides of MATLAB is that it is proprietary. So, whether you want to use MATLAB for personal, academic, or business purposes, you must purchase a license.
A creation of Apple Inc., Swift is a robust and intuitive programming language for iOS, macOS, iPadOS, watchOS, and tvOS. It is fast, safe, and interactive, with code optimized and compiled to make the most of modern hardware. Swift is interoperable with Python and compatible with TensorFlow with a modern and lightweight syntax.
Swift is no longer limited to iOS systems and works on Linux platforms. It provides various libraries for numerical computations, digital signal processing, high-performance matrix math functions, building machine learning models, and more.
While there are several data science languages, choosing the best one for your data science career path can be overwhelming. Consider the following factors before choosing the programming language you want to work with:
- The goal you are trying to accomplish
- How data science can help you execute the task at hand
- Your experience with programming
- Your skill in the programming languages you already know
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Which language is required for data science?
Is Python enough for data science?
Python may be enough for data science as a programming language. Still, you need to know other languages such as SQL to frequently process large data volumes that businesses have to deal with regularly.
Is R challenging to learn?
R is a simple and easy-to-use programming language. However, it has a steeper learning curve than Python but gets easier once you learn to use R’s features.