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5 Best Programming Languages for Data Science

Python, R, and SQL are among the most important programming languages for data science. Learn more about essential coding languages for data science with Rice.

Two data science programmers coding and collaborating on ML models together at work

If you’re pursuing a career in Data science or big data analytics, you must have at least basic programming skills. This means being well-versed in the most important Data science programming languages, which are sets of detailed instructions or commands used to communicate with computers and direct them to build models or perform certain functions.

This article will highlight the 5 most important data science programming languages used by Data scientists and explain each language’s advantages and disadvantages.

The best Data science coding languages include:

  • 1. Python
  • 2. SQL
  • 3. R
  • 4. VBA (Visual Basic for Applications)
  • 5. Julia

There are some additional Data science coding languages worth mentioning, which add value in specialized situations or have less adoption due to newness. These include:

  • 6. Java
  • 7. SAS
  • 8. MATLAB
  • 9. Scala
  • 10. Javascript
  • 11. C/C++
  • 12. Swift

How is Programming Used in Data Science?

In data science, coding languages are used across all job roles. They enable Data scientists to pull data from multiple datasets, clean and analyze that data, visually convey the importance of the data, and design databases and machine-learning algorithms. The best programming language for you will depend on your role as a Data scientist, specific project goals, and your level of experience.

5 Most Important Programming Languages for Data Science

Below, we’ll go into each programming language and help you understand how these languages are used in various applications in the Data science field.

1. Python

Python has been among the most popular data science languages in the last several years. A high-level, general-purpose, open-source programming language, Python’s syntax is easy to follow and write. This means that Data scientists without a strong coding background can learn Python and start using it quickly. There are also extensive libraries in Python – collections of files (or modules) that contain pre-built code to help Data scientists with common tasks. These libraries can make tasks like data cleaning, data analysis, data visualization, and machine learning-related functions easier. Machine learning libraries, specifically, are of high value to Data scientists because they offer open-source access to just about any machine learning algorithm the Data scientist may seek.

Many Data scientists use Python, but it is especially useful for those who specialize in machine learning because of the extensive machine learning libraries.

Python has an English-like syntax, which makes it easier to read and understand than some other programming languages. It typically takes less time to learn because users require fewer lines of code to perform the same task compared to other common programming languages and it automatically assigns data types. Because of this simplicity, it’s often considered more productive. It’s free and open-source, which is a plus for those who want to modify specific behaviors. Python is also portable, meaning code can be run on any platform once written.

Some disadvantages include slower speed due to its dynamic, line-by-line execution of code, and high memory usage. Python has some shortfalls in client-side or mobile applications because of its memory inefficiency and slower processing. Python also has less-developed database access than other languages and more frequent runtime errors.

2. SQL

Structured query language, known as SQL, is an essential language for Data scientists managing relational databases and working with large amounts of structured data. SQL allows a Data scientist to query, perform analysis of and manipulate data within relational databases and create test environments for data.

SQL may be easier to learn than other data science programming languages because it uses a simple structure with English words, and its short syntax allows Data scientists to effectively query, get insights from and manipulate structured data. SQL also integrates easily with languages like Python and R, so a Data scientist might use SQL to query specific data from a database and then use Python or R to perform a deeper analysis on the retrieved data. Since most database management systems are SQL-based, the language is an important one to learn if you’re looking for a data science career. It’s often a required skill for most data-centered jobs.

3. R

Also ranking among the top data science programming languages, R is more specialized than Python. It is useful in Data scientist roles and tasks focused on data mining and statistical analysis. R can handle large data sets and complex processing, and – especially for those Data scientists with statistics backgrounds – can be very intuitive when analyzing data and communicating results.

While it is the most used programming language for statistical functions, R has advantages and disadvantages. It is open-source, platform-independent, and has a large collection of libraries. It supports various data types, and it is useful for Data scientists responsible for data cleansing, data wrangling, and web scraping. Its libraries offer access to high-quality graphs, visualizations, and resources to help process large data sets using parallel or distributed computing. R does not require a compiler to turn code into an executable program, is compatible with other programming languages, is used in machine-learning applications, and has a comprehensive development environment.

Some potential disadvantages include that R is often more difficult to learn than other programming languages, tends to function slower, and takes up a lot of memory. It doesn’t have robust security or a dedicated support team, and its lack of code-design guidelines can result in programs with poor readability.

4. VBA (Visual Basic for Applications)

VBA, or Visual Basic for Applications, is an event-driven programming language developed by Microsoft, accessible through its Microsoft Office suite of products like Excel, Visio and PowerPoint. If you've ever used or heard of "custom macros," (short for "macroinstructions"), you were using VBA. VBA is also built (at least partially) into other popular business applications such as ArcGIS (web-based mapping software) and AutoCAD (drafting, design and modeling software).

Because Microsoft Office is ubiquitous in business, VBA is a practical and intuitive "beginner" or transitional language for data analysis, computing, and automation. For example, a Financial analyst or Data scientist in the financial services or insurance sectors might use VBA in excel to build risk management models or investment prediction tools using large amounts of business data. A Data scientist in the logistics and transportation sector might use VBA within ArcGIS to analyze the efficiency and safety of a company's fleet vehicles in aggregate.

One disadvantage is that VBA can only function within its host application like Excel or ArcGIS. It does not function as a standalone software application. It's also not as powerful as Python, but it's a great tool for Data analysts, Business analysts or Financial analysts making the transition into Data science.

5. Julia

Designed for computations and numerical analysis, Julia can be an important programming language for Data scientists focused on data visualization, deep learning, numerical analysis, or interactive computing. It is faster than some other languages, like Python, and more effective in distributed and parallel computing. It’s a common language for Data scientists focused on big data analysis.

As a high-level and general-purpose language, Julia can allow Data scientists to write and quickly implement executable code. For Data scientists involved in scientific computing, machine learning, data mining, large-scale linear algebra, and distributed and parallel computing, it’s an important programming language to understand. It’s fast and features a simple syntax for mathematics operations and automatic memory management.

Because it is relatively new, one disadvantage of Julia is that its developer community is relatively small, and there are fewer tools for debugging.

7 Additional Coding Languages for Specific Data Science Use Cases

Below, we’ll go into each programming language and help you understand how these languages are used in various applications in the Data science field.

6. Java

Java is a different programming language than JavaScript. It can often be found within desktop and web enterprise applications, credit card programming, and Android apps. Java code is compiled and used to develop virtual machine or browser applications, while JavaScript is an all-text language that runs only on browsers. Many technology companies use Java in their software.

Among data science programming languages, Java is considered relatively simple to learn, use, write, compile, and debug. It is object-oriented, allowing Data scientists to create standard programs and reusable code, and it runs on any machine with JVM. Java can be used in distributed computing, features an effective security manager, allocates memory into heap and stack, and is multithreaded so a program can perform multiple tasks at one time.

Disadvantages include its slower speed and greater memory consumption than other programming languages.

As a Data scientist, you may use Java for importing and exporting data, cleaning data, statistical analysis, machine- and deep learning, text analytics, and data visualization.

7. SAS

Although its associations with data science are less common than other programming languages, requirements for understanding SAS exist in some data science or analyst roles in finance, manufacturing, healthcare, and other industries using older systems.

SAS, an acronym for statistical analytics software, can retrieve, report, and analyze statistical data. It is an expensive, closed-source proprietary tool tailored to meet specific industry demands. Known for its efficiency and stability, SAS is primarily used by large-scale corporations.

SAS is an important programming language for Data scientists looking for jobs at large companies specializing in business intelligence.

8. MATLAB

MATLAB can be an important data science programming language for those working in academia, scientific research labs, or potentially in the aerospace, automotive, or robotics industries. Specific to mathematical and statistical computing, MATLAB provides built-in tools for dynamic visualizations and a deep learning toolbox that helps Data scientists face challenging mathematical processes. It is a scalable language and offers built-in visualization graphics.

As a combination language and working environment, MATLAB is relatively easy to use. It is supported on a variety of platforms, features a library of predefined functions and solutions, and offers many plotting and imaging commands. Some drawbacks include its slower execution as an interpreted versus a compiled language and it tends to be a more expensive option.

9. Scala

Designed as a more streamlined alternative to Java – which is discussed below – Scala is useful for Data scientists analyzing large sets of data known as big data processing. It supports object-oriented and functional programming, as well as scripting language used to build applications for the Java Virtual Machine (JVM), which allows Java programs to run on any device or operating system while optimizing program memory.

Scala’s advantages include a simple learning curve for Data scientists who already have experience in Java or similar programming languages. It has a strong lineup of integrated development environments (IDEs); it’s scalable and it works well with other data analytics tools. Scala is highly functional, which means you can be more productive by writing fewer lines of code with few errors or disruptions. Many big companies have adopted Scala because of its scalability.

There are some potential disadvantages associated with Scala. Sometimes, its two approaches – both object-oriented and functional – can make it hard to understand compared to other data science programming languages for less experienced users. There are a limited number of Scala developers compared to Java.

10. Javascript

JavaScript is a general-purpose programming language that helps Data scientists develop dashboards and visualizations based on big data insights. Compared to other data science languages, JavaScript tends to be faster because it’s an interpreted language that’s easy to understand and learn. Javascript integrates well with other programming languages and third-party add-ons that allow for the use of predefined code. JavaScript is capable of front-end and back-end development and often improves web application performance by reducing code length.

The fact that it’s a client-side script can be an advantage and a disadvantage. An advantage is that it speeds up execution because data validation is possible on the browser versus sending it to the server. A disadvantage is that because JavaScript code is viewable to the user, malicious users may access the source code without authentication or place code into the site that compromises data security.

Other potential drawbacks of Scala include less efficient debugging, limited functionality on certain browsers, only single inheritance support, and the ability of a single code error to stop the rendering of the entire JavaScript code.

Data scientists can use JavaScript for handling real-time data, visualizations, and asynchronous tasks.

11. C/C++

In data science, the programming language C/C++ helps programmers develop and fine-tune statistical and data tools. C is a general-purpose language, and C++ is an object-oriented language. Both can be helpful for Data scientists, as major machine learning libraries are often written in these languages.

C is considered one of the closest languages to the inner workings of computers, and it is a fast language to compile. As a Data scientist, you might use it to implement machine learning algorithms that require a great deal of processing time.

C++ has rapid processing capabilities and is the only programming language that can be compiled over a gigabyte of data in less than a second. Therefore, it is useful for Data scientists taking on large, data-driven tasks. C++ is also an efficient programming language for developing new programming libraries that can be used with other language applications.

Disadvantages include that these languages may be complex to learn and understand, they require manual memory management, their coding doesn’t support built-in code threads, and they come with several potential security issues.

12. Swift

As a programming language, the fairly new Swift is quite a bit faster than Python and very close to C in speed. It has a simple, readable syntax and is more efficient, stable, and secure than Python. Swift is the official language for developing iOS applications for the iPhone, among other high-profile uses. Swift features robust integrated support for automatic differentiation allowing computers to accurately compute the partial derivative of a value of a function quickly.

Data scientists who strive to be deep learning researchers should likely be most concerned with learning Swift, as it’s projected to play a big role in this area.

Consider Rice University's Top-Rated Online Data Science Offerings

Master's Program in Data Science: As part of the online MDS curriculum, students strengthen their data science coding skills using programming languages like Python, R, and SQL among others. The Admissions committee prefers applicants with some basic understanding of these programming languages for Data science.

Data Science Beginners: If you have zero prior programming experience, Rice offers 2 options: