Data science is one of the most popular and demanded fields today. There is a huge demand for data scientists, and the demand is expected to grow over the next few years.
The main task of a data scientist involves a lot of programming. As a result, there are thousands of programming languages that are being introduced and accepted every day.
The best programming languages to learn depends on your niche. But if you’re learning a programming language to help with your future data science career, then which language should you choose?
This blog will discuss the latest and best programming languages for data science that might change the game in the next 5 to 6 years.
Why programming languages are important for data science?
Programming languages are important for data science because data scientists need to write their own code sometimes. Sometimes you need to write code to link your data with your analytical results.
Data is everywhere these days. It’s in our emails, our phone calls, and even our tv shows! Data analysis has become a popular topic in recent years as people realize how much insight we can gain from data.
However, oftentimes, the process of collecting and analyzing data can be difficult! This is because different data have different attributes, and data analysts often need to use a specific programming language.
Also Read: What are the Best Ways to Learn Programming
Best Programming Languages for Data Science
The following are the best programming languages for data science that data scientists should be familiar with:
Python is a general-purpose programming language that can be used to create virtually any type of software. It is considered one of the best programming languages for data science.
It is well-known for its concise syntax, readability, and code portability. It’s also open-source and works on all major platforms, which makes it popular among developers.
Python is simple to learn and has a large developer community behind it. It’s also powerful enough for professional data scientists to use.
Features of Python Programming:
- Python is a high-level programming language that is simple to learn.
- It has a large standard library that anyone can use.
- It is portable, which means that it can run the same code on multiple machines.
Java is a popular choice for creating websites and applications from scratch, it has recently gained popularity in the data science industry. This is primarily due to Java Virtual Machines, which provide a strong and efficient foundation for popular big data tools like Hadoop, Spark, and Scala.
As a result, Java is the best data science coding language.
Features of Java Programming:
- Java programming language is easy to learn, fully understand, read, and write.
- It supports both program compilation and interpretation.
- Java allows for multithreading programming.
C/C++ is a data science programming language that is used to create computer applications. It is a low-level programming language that is used in high-performance applications such as games, web browsers, and operating systems.
In addition to its widespread use in application development, C/C++ is used for numerical computations.
Features of C++/C Programming:
- C++ is a platform-independent programming language.
- It allows for dynamic memory allocation.
- It is a fast language because of its short compilation and execution times.
Scala has quickly become one of the most popular and best programming languages for data science and AI applications.
It is often considered a hybrid language for data science between object-oriented languages like Java and functional ones like Haskell or Lisp because it is statically typed and object-oriented.
Furthermore, Scala has many features that make it appealing to data scientists, such as functional programming, concurrency, and high performance.
Features of Scala Programming:
- Scala does not require you to explicitly specify the data type and function return type.
- Its computation by default lazy.
- It comes with a standard library that includes the actor model.
R is a statistical programming language that is widely used for data visualization, statistical analysis, and other data manipulation tasks. R has grown in popularity among data scientists.
Furthermore, the R programming language for data science provides many packages for machine learning algorithms, such as linear regression, random forest, neural networks, k-nearest neighbor algorithm, and so on.
Features of R Programming:
- R is a well-developed, simple, and effective programming language.
- It has a robust data handling and storage system.
- R includes a set of operators for performing calculations on arrays, lists, vectors, and matrices.
SQL is one of the most widely used and best programming languages for data science in the world. It is a declarative database language that enables you to write queries to extract information from your data sets.
SQL is used in almost every industry, so learning it early in your data science journey is a good idea. Its commands can be run interactively from a terminal window or via embedded scripts in other software applications such as web browsers or word processors.
Features of SQL Programming:
- SQL provides users with flexibility and scalability when it comes to relational database management systems.
- It provides extensive transactional support.
- SQL’s high security is another notable feature.
Go is a data science programming language that is also known as GoLang. This programming language is slowly gaining popularity and is useful in machine learning projects.
It was introduced to the world by Google in 2009. People refer to this as the next step in the C language hierarchy because it has a syntax that is very similar to C.
When it comes to Data Science, this programming language is extremely beneficial in ML operations.
Features of GO Programming:
- Go is a very simple language to learn.
- It allows new programmers to quickly learn the language.
- It is an open-source programming language.
This is one of the best programming languages for data science in 2023.
MATLAB is an interactive environment and high-level language for visualization, numerical computation, and programming.
Many languages are required for data science. MATLAB allows you to manipulate matrices, plot functions, and data, implement algorithms, create user interfaces, and extend existing software.
Features of Matlab Programming:
- MATLAB offers an interactive environment for iterative exploration, design, and problem-solving.
- It includes graphics to improve the user experience.
- Object-oriented programming is supported by MATLAB.
- It is platform-independent, also known as portable.
We hope you enjoyed this blog about best programming languages for data science. In this blog, we will explain the top 9 programming languages for data science.
As a data scientist, you will have to find the right tool to analyze data. Programming languages have evolved with the growth of technology. Python, R, and Scala are the most popular programming languages in the field of data science. However, Python is the most popular and best programming language for data science in 2023. It is popular for statistical analysis and machine learning.
Frequently Asked Questions
Is Python or C++ better for data science?
It is capable of machine learning and data analysis, but it is no match for Python. Python’s user-friendly syntax makes it a better choice for beginners. C++ necessitates knowledge of various programming conventions and necessitates additional research and time to learn.
Is C++ needed for data science?
Data Science does not necessitate the use of C++. Knowing the fundamentals of C++ or Java, on the other hand, may help you understand some of the fundamentals of Python.