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In this article, we look at some of the top data science programming languages for 2023, and present the strengths and capabilities of each of them. Python; R; SQL; Java; Julia; Scala; C/C++; JavaScript; Swift; Go; MATLAB; SAS; All data has been updated to demonstrate the latest trends for 2023 and beyond. 12 Top Data Science Programming ...
- Stephen Gossett
- Senior Staff Reporter
- Python. How Python is used in data science: Python has become widely used in data science for areas like data manipulation, data analysis (NumPy, SciPy, Pandas) and data visualization (Matplotlib, Seaborn, Bokeh).
- R. How R is used in data science: R is still considered best-suited for data mining and statistical analysis, of which it offers a wide range of options.
- SQL. How SQL is used in data science: SQL sometimes isn’t even considered a proper programming language since it’s domain-specific, though it remains a must-know in the realm of database management.
- C/C++ How C/C++ is used in data science: General-purpose language C and its object-oriented counterpart, C++, offer significant lift for high-performance data science applications and projects.
- Python. Python is a popular data science programming language because of its simple syntax and intuitive features. This also makes it the perfect choice for beginner programmers.
- Javascript. Javascript was originally used to create dynamic interactions for web applications. But it is now widely used in data science because of libraries like Tensorflow.js and its machine learning capabilities.
- R. R is among the most powerful programming languages for statistical computing. It allows you to build statistical models and carry out complex calculations with ease.
- Java. Java is among the most widely used programming languages in the world. It is a highly efficient and versatile language that performs well across device types, making it a good choice for data science applications.
Best Programming Languages for Data Science. 1. Python. 2. 3. Julia. 4. SQL. 5. Scala. 6. C++. 7.MATLAB. 8. Java. 9. Javascript. 10. SAS. 11. Go. How much coding do you need for data science? Do you need to be an expert at coding? Is coding required for data science? Yes.
- Python. As discussed previously, Python has the highest popularity among data scientists. This is due to its wide range of uses. It is often the go-to choice for a range of tasks for domains, such as, machine learning, deep learning, artificial intelligence and other popular forms of technology.
- JavaScript. JavaScript is the most popular programming language to learn. It is most commonly used for web development due its capability of building rich and interactive web pages.
- Java. Java, which is sometimes referred to as “Write Once, Run anywhere” is a programming language that has been used by top businesses for secure enterprise development and is now being used for tasks involving data analysis, data mining, and machine learning.
- R. R is quickly rising the ranks as one of the most popular programming languages for data science, and for good reason. R is a highly extensible and easy to learn language that fosters an environment for statistical computing and graphics.
Find out which programming languages are essential for data science jobs and which ones provide the best opportunity to become a successful data scientist. ...
Aug 16, 2023 · Data science professionals use programming languages such as R, Python, SQL, JavaScript, and C/C++. Each data science language has unique functions with advantages and disadvantages. Some languages are better suited to data science than others.
