Whether you are new to coding or have years of experience in the field, learning a programming language is one of the easiest and most versatile ways to get into data science or to improve your skills. Generally, computer programming is used to create instructions or commands for a computer to follow. Within data science, these programs can be used for a variety of actions, such as analyzing a collection of data or creating visualizations, like graphs and charts. With that being said, programming languages are how data scientists speak to the computer through specific types of code. Each programming language has its own unique grammar, syntax, and structure for giving commands to the computer.
Due to this fact, programming languages range in complexity and accessibility amongst users. In the past, computer programming was primarily relegated to individuals that had advanced education or degrees in science and technology, like computer science and engineering, but in the 21st century, many people can learn programming languages through taking online courses, bootcamps, or certificate programs. The following list outlines the most popular programming languages within the data science industry, and how you can gain enough knowledge of these languages to begin or expand your data science career.
Top 8 Programming Languages You Should Learn
These eight programming languages are the best to learn if you want to begin developing or expanding your data science skills. Each of these languages has several technological affordances that data scientists need, such as high-level processing and analysis of big data, storage capacity, and organizational capabilities, as well as multiple styles of visualization and modeling. In addition, most of these languages are open source and rely on English syntax, making them easily accessible to users of all experience levels.
Python is one of the most popular programming languages that you can learn and is generally ranked as the second or third most widely used language by users around the world. For data scientists, Python is especially popular because you can use it to complete multiple tasks, from the initial steps of storing collected data to the final stages of visualization and sharing. As an open-source programming language with thousands of libraries and an ever-growing community of users, there is also a lot of support and resources for individuals that want to learn and apply Python in their work. Due to its popularity, positions for data scientists and developers that specialize in Python are quite common.
Similar to Python, R is another incredibly popular programming language that is widely used by computer programmers, engineers, and data scientists. As a programming language that relies on statistical analysis, such as regression, the R software is a favorite amongst researchers and data practitioners that are working with more complex data sets. Through RStudio, students and professionals can use this programming language to make it easier to program their data-based code.
Larger data sets require a database that can not only hold that data set but also give you easy access to what it is in the database and what the data means. Widely recommended for database design and management, the SQL programming language is especially beneficial for data scientists that want to learn how to search, organize, and model large stores of data. SQL is popular amongst data scientists that work in the realm of data collection and archives, such as government, healthcare, and libraries.
This programming language has also seen increased popularity amongst data scientists due to its uses within machine learning and multiple digital media platforms. As the #1 programming language, Java offers users access to cloud-based computing services that are compatible with multiple programming languages and libraries. For data scientists that work with audience data, mobile applications, and social media, Java provides opportunities to use data in new and innovative ways.
As a programming language that is compatible with Java, Scala is another great programming language for working with big data. This is because Scala is an object-oriented programming language that is widely used to find patterns and trends within a dataset through the design of functions and querying. Through these functions, you can use Scala to search through a data set and discover important information and findings, which is the primary purpose of most data science research and projects.
Predating languages like R and Python, C++ is considered a core competency within the community of computer programming, as this language has been used for decades amongst more experienced programmers and is the starting point for many other types of code. Not only useful for complex computing tasks, this programming language also has data science-related capabilities. Similar to Python, C++ has multiple libraries and is highly compatible with many types of programs and interfaces. When using C++ for data science, it is common to see this language used when working with private data or compiling multiple packages and data stores.
Considered to be the programming language of choice for academics and researchers in the sciences, as the name indicates, MATLAB is primarily used for mathematical operations and statistical analysis, such as creating formulas, functions, and graphs/charts. In addition, MATLAB is primarily used within the realm of machine learning and artificial intelligence because the programming language can be used to automate specific programs and data processes.
Want to learn more about these top programming languages?
Noble Desktop offers multiple data science classes which specialize in teaching both students and professionals Python, R, SQL, and many other programming languages covered on this list. Whether you are interested in taking live online data science classes with a remote instructor, or in-person data science classes in your area, there are multiple ways for you to stay informed about the latest data science tools and technologies!