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Explore 12 Powerful Data Science Programming Languages in 2025!

Explore 12 Powerful Data Science Programming Languages in 2025!

Updated on Aug 21, 2025 | 16 min read | 100.36K+ views

Did You Know that In 2025, nearly 90.6% of data science professionals use Python regularly across varied programming tasks. This reflects Python’s importance in data science programming for machine learning, automation, and scalable data processing.

The top data science programming languages in 2025 include Python, R, SQL, and Julia, among others. These languages are vital for data scientists as they are specifically designed to handle tasks such as data analysis, machine learning, and big data processing.

In data science, it's crucial to understand the strengths of each programming language. Supported by a diverse range of R libraries in data science, R offers robust solutions for statistical analysis and visualization, making it a preferred choice for many analytical workflows.

In this blog, we'll explore why these languages matter, what makes them unique, and how to choose the right one for your project.

As data science continues to grow, learning languages like Python, R, and SQL is essential. upGrad's Data Science Course provide hands-on training to help you overcome data challenges and advance your career. Enroll today!

12 Top Data Science Programming Languages

Data science programming languages each have distinct strengths. Python is a go-to for machine learning and automation due to its extensive libraries like TensorFlow and Scikit-learn.

R stands out in statistical analysis and data visualization, with packages like ggplot2 and dplyr that allow for advanced data manipulation. SQL is essential for querying large databases efficiently and supports optimization techniques commonly used in areas such as linear programming in data science.

Learn from leading experts, master essential tools and techniques, and build job-ready skills through hands-on projects and real-world applications.

The choice of data science programming language depends on project needs, such as data type, model complexity, and performance requirements.

Must-read: Step-by-Step Guide to Learning Python for Data Science

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Let's now explore the top 12 data science programming languages commonly used in data science, along with their primary use cases.

Learn the tools and programming languages that power modern data science with these career-ready courses:

Let’s now explore the features of some popular programming languages that can maximize efficiency, improve scalability, and ensure the success of your data science efforts.

1. Python

Python is one of the most popular and versatile data science programming languages, widely known for its simplicity and readability. It stands out in data science due to its vast ecosystem of tools and frameworks. Commonly used Python libraries for data science,such as Pandas, NumPy, Scikit-learn, and TensorFlow, make data processing efficient.

Python’s ability to handle complex tasks with minimal code, combined with its support for multiple programming paradigms, makes it a preferred choice for data scientists.

Real-World Applications:

Data Analysis: Python is used for handling, cleaning, and transforming large datasets, making it a go-to tool in industries like finance, healthcare, and e-commerce.
Machine Learning: Python supports building advanced machine learning models with libraries like TensorFlow, Keras, and Scikit-learn. It's used by companies like Google and Facebook for deep learning and AI development.
Data Visualization: Python helps create detailed and interactive plots for visualizing data using libraries like Matplotlib and Seaborn, often used by analysts to present data insights effectively.

Advantages and Disadvantages:

Advantages	Disadvantages
Python’s easy-to-read syntax promotes quick learning and better collaboration.	Python is slower than compiled languages like C++ or Java, especially for computation-heavy tasks.
With libraries like Pandas, NumPy, and Matplotlib, Python offers a rich ecosystem for data science.	Python can use more memory than other languages, which might be an issue for large-scale data.
Python can be used for data science, web development, automation, and scripting.	Python’s dynamic typing can lead to runtime errors that are difficult to catch during development.

To gain a deeper understanding of Python, upGrad’s Learn Basic Python Programming course is the perfect choice. The 12-hour free program will help you learn the fundamentals of Matplotib, Python programming, and more.

Also Read: Top 10 Reasons Why Python is So Popular With Developers in 2025

2. SQL

SQL (Structured Query Language) is a domain-specific language used to manage structured data within relational databases. It enables efficient querying, updating, and managing of data, making it indispensable in data science for handling large datasets.

SQL is a critical tool for data scientists, especially when dealing with large volumes of structured data stored in relational databases.

Real-World Applications:

Data Retrieval: SQL is widely used for querying large datasets and extracting specific data points, such as pulling sales figures or customer data from relational databases.
Database Management: SQL allows for creating, updating, and managing database structures, such as creating new tables, updating records, or deleting obsolete entries using commands like CREATE, ALTER, and DROP.

Advantages and Disadvantages:

Advantages	Disadvantages
SQL can efficiently retrieve large volumes of data from complex databases.	Advanced SQL features, such as subqueries, joins, and window functions, can be challenging for beginners.
SQL is standardized, making it universally supported across relational database platforms, ensuring portability.	SQL is not ideal for complex statistical analysis or machine learning tasks, which require specialized tools.
SQL offers built-in features for data integrity, transaction management, and access control, ensuring secure data handling.	It requires a deeper understanding for handling complex operations or optimizing queries, which may overwhelm beginners.

Learn advanced SQL techniques with upGrad’s Advanced SQL course, where you'll learn to use functions and formulas to query and manipulate data. These skills are critical for extracting insights from large databases and performing complex data analysis, making them essential for any data scientist.

Also read: Free SQL Certification Course Online [2025]

3. R

R is a programming language tailored for statistical computing and data analysis, making it one of the most widely used data science programming languages. It provides a comprehensive suite of tools for statistical modeling, data manipulation, and visualization.

R is particularly popular in fields like academia, bioinformatics, and economics for analyzing complex datasets and creating informative visual representations.

Real-World Applications:

Statistical Analysis: R excels in conducting advanced statistical analyses, such as regression, hypothesis testing, and time-series analysis. It’s used to analyze data across fields like healthcare, finance, and social sciences.
Data Visualization: With libraries like ggplot2 and lattice, R offers some of the best visualization tools, enabling data scientists to create customizable charts, graphs, and plots.
Machine Learning with R: R is also used for building predictive models and performing machine learning tasks with libraries like caret, randomForest, and xgboost.

Advantages and Disadvantages:

Advantages	Disadvantages
R is powerful for statistical analysis, making it ideal for data scientists and statisticians working with complex datasets.	R has a steeper learning curve for beginners, particularly those with limited programming or statistical background.
The large community and active support make it easy to find resources, new packages, and tools for advanced analysis.	R’s memory management is less efficient than some languages, especially when working with large datasets.
R has rich visualization tools, such as ggplot2, providing high-quality graphics for data presentation and analysis.	R is less suitable for general-purpose programming compared to languages like Python.

Also Read: R For Data Science: Why Should You Choose R for Data Science?

4. VBA (Visual Basic for Applications)

Excel VBA (Visual Basic for Applications) is a programming language developed by Microsoft, designed to automate tasks within Office applications like Excel, Access, and Word. It allows users to write custom code to automate repetitive tasks, generate reports, and streamline business processes.

VBA is particularly popular in business environments where data manipulation within Microsoft Office applications, especially Excel, is critical for day-to-day operations.

Real-World Applications:

Data Cleaning: VBA automates tasks such as removing duplicates, handling missing values, and applying data transformations in Excel, reducing manual effort and errors.
Report Generation: VBA enables the automation of creating customized reports and dashboards, often used to generate weekly or monthly reports from large datasets.
Data Processing: Writing macros with VBA allows users to process large datasets, perform aggregations, and manipulate data efficiently within spreadsheets.

Advantages and Disadvantages:

Advantages	Disadvantages
VBA is easy to learn for users already familiar with Excel, making automation accessible to non-programmers.	VBA is not suitable for large-scale data analysis or complex machine learning tasks.
It automates repetitive tasks in Excel, saving time and reducing human error in data analysis and reporting.	VBA is platform-dependent, working primarily within the Microsoft Office ecosystem.
Widely used in business environments to automate reporting and streamline processes, making it valuable for business analysts.	Performance can degrade when working with large datasets or complex computations.

Also Read: 33+ Data Analytics Project Ideas to Try in 2025 For Beginners and Professionals

5. Julia

Julia is a high-level programming language designed specifically for technical and numerical computing. Known for its speed and flexibility, Julia is increasingly popular for large-scale data analysis, machine learning, and scientific computing tasks.

Unlike other languages, Julia is preferred for intensive computation tasks due to its impressive performance, making it ideal for industries requiring high-performance computing like finance, physics, and bioinformatics.

Real-World Applications:

Numerical Analysis: Julia excels in tasks like linear algebra, differential equations, and optimization, making it a go-to language for scientific researchers and engineers.
Machine Learning: Julia supports machine learning through libraries like Flux.jl and MLJ.jl, helping users build models ranging from simple regressions to complex deep learning systems.
Big Data Processing: Julia handles large datasets efficiently, making it a top choice for big data analytics and high-performance computing, especially in research and industry environments.

Advantages and Disadvantages:

Advantages	Disadvantages
Julia's performance is comparable to low-level languages like C, making it ideal for computationally expensive tasks.	Julia is still relatively new, with some areas of its ecosystem still maturing.
Its user-friendly syntax makes Julia easy to learn, even for beginners.	Libraries for web development and GUI design are still underdeveloped.
Julia offers native support for parallel and distributed computing, making it ideal for large-scale tasks.	Julia has a steeper learning curve for those new to programming languages.

Also Read: Programming Language Trends in Data Science: Python vs. R vs. SQL Usage Stats

6. JavaScript

JavaScript is a widely used programming language, particularly in web development. It enables data scientists to create interactive visualizations and data-driven web applications, making complex datasets more accessible. With powerful libraries like D3.js and Chart.js, JavaScript is essential for building dynamic and visually engaging representations of data..

Real-World Applications:

Data Visualization: JavaScript excels in creating interactive and dynamic visualizations, enabling users to explore complex datasets using libraries like D3.js and Plotly.js.
Real-Time Data Processing: JavaScript, along with Node.js, is ideal for handling real-time data streams, making it useful for applications requiring up-to-date information.
Building Web-Based Dashboards: JavaScript is commonly used to develop dashboards that display real-time analytics and provide an interactive interface for data exploration.

Advantages and Disadvantages:

Advantages	Disadvantages
JavaScript excels in creating interactive data visualizations, ideal for web-based applications.	JavaScript is not ideal for complex data processing tasks like deep learning model training.
Its real-time data processing capabilities make it great for building real-time analytics applications.	Advanced JavaScript features like asynchronous programming can have a steep learning curve.
JavaScript’s cross-platform compatibility allows it to run on various devices, including mobile phones and desktops.	While popular in web development, JavaScript is less commonly used for traditional data science tasks.

Start your journey with JavaScript through upGrad’s JavaScript Basics from Scratch course. The 19-hour free program will help you gain expertise on variables, conditionals, and more for industry-relevant tasks.

upGrad’s Exclusive Data Science Webinar for you –

7. Java

Java is a high-level, object-oriented programming language known for its portability, performance, and scalability. It is often used to build enterprise-level data science applications and handle big data processing tasks. With frameworks like Hadoop and Apache Spark, Java efficiently manages large-scale datasets and complex systems, making it a go-to language for big data solutions.

Real-World Applications:

Big Data Processing: Java is commonly used with Apache Hadoop and Apache Spark to process massive datasets, enabling efficient analysis in data-driven environments.
Machine Learning: Java supports machine learning with libraries like Deeplearning4j, Weka, and MOA, making it suitable for building machine learning models and AI systems.
Data Engineering: Java is ideal for creating robust data pipelines that efficiently process and handle large volumes of data, ensuring smooth data flow in enterprise systems.

Advantages and Disadvantages:

Advantages	Disadvantages
Java's performance is enhanced by the Just-In-Time (JIT) compiler, making it ideal for large-scale data processing.	Java’s verbose syntax can make coding more complex and lengthy compared to other languages.
Java's cross-platform compatibility allows applications to run on any device with a JVM, promoting portability.	Java is not well-suited for statistical analysis, lacking the extensive libraries that other languages offer.
Java’s multithreading capabilities enable efficient concurrent processing, essential for big data analysis.	Java’s rigid structure and verbose syntax can slow down development speed, especially for rapid prototyping.

Strengthen your programming foundation for data analysis with upGrad’s Core Java Basics course. The 23-hour free program will help you learn IntelliJ, basics of coding, data strategy and more for data science.

Also Read: Library Management System Project in Java: Design & Features

8. Scala

Scala is a general-purpose programming language that combines the best features of functional and object-oriented programming. It is known for its concise syntax and is widely used in big data processing tasks, especially with Apache Spark.

Scala helps data scientists efficiently manage, analyze, and process large datasets in distributed environments, making it a top choice for data engineers working on big data applications.

Real-World Applications:

Big Data Processing: Scala is commonly used with Apache Spark for big data processing tasks, enabling efficient distributed data handling and real-time analytics.
Machine Learning: With libraries like Spark MLlib and Breeze, Scala provides robust tools for building machine learning models and performing statistical computations.
Data Analysis: Scala is also employed for data wrangling, manipulation, and analysis, providing a powerful environment for working with large datasets.

Advantages and Disadvantages:

Advantages	Disadvantages
Scala’s concise syntax allows developers to write more expressive and readable code.	Scala's steep learning curve can be challenging for beginners, especially due to its combination of functional and object-oriented programming.
Scala is fully interoperable with Java, making it easy to integrate with existing Java libraries and frameworks.	Scala can have slower compilation times compared to languages like Java.
Optimized for big data applications, Scala is perfect for large-scale distributed data processing with Apache Spark.	Scala's ecosystem is smaller than that of other more widely used programming languages.

Also Read: Scala vs Java: Understanding the Differences and Similarities for 2025

9. SAS

SAS (Statistical Analysis System) is a powerful software suite used for data management, predictive analytics, and advanced statistical analysis. Widely used in sectors like finance, healthcare, and retail, SAS provides essential tools for processing and analyzing large datasets, making it indispensable in data-heavy industries.

Real-World Applications:

Data Manipulation: SAS is highly effective at cleaning, transforming, and managing large datasets, ensuring efficient data preparation for analysis.
Statistical Analysis: SAS excels in complex statistical analysis, including regression, ANOVA, time-series analysis, and survival analysis, making it a top choice for in-depth data studies.
Predictive Modeling: SAS supports a wide range of predictive analytics techniques, including decision tree algorithms, linear and logistic regression, and neural networks, aiding businesses in forecasting and decision-making.

Advantages and Disadvantages:

Advantages	Disadvantages
SAS provides a comprehensive suite of analytical tools for predictive modeling and statistical analysis.	SAS is expensive, with a high cost that can be prohibitive for smaller organizations or individual users.
Its scalability allows SAS to handle large datasets, making it ideal for enterprise-level data analysis.	SAS’s visualization tools are not as advanced or user-friendly as those in other data science programming languages.
SAS is known for its consistency, reliability, and a long-standing presence in the data science community.	SAS can be slow to adopt new trends in data science, such as deep learning and machine learning techniques.

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10. MATLAB

MATLAB (Matrix Laboratory) is a high-level programming language widely used for numerical computing, data analysis, and visualization. Known for its advanced algorithms and powerful toolboxes, MATLAB is particularly favored in research, engineering, and scientific computing.

It excels in machine learning, signal processing, and complex data analysis tasks, providing a reliable environment for professionals dealing with highly technical data.

Real-World Applications:

Data Analysis: MATLAB provides efficient tools for data manipulation, exploration, and analysis, making it a go-to for engineers and scientists working with complex datasets.
Machine Learning: MATLAB supports machine learning algorithms like decision trees, support vector machines (SVMs), and deep learning models, providing a solid foundation for AI projects.
Data Visualization: MATLAB is renowned for its ability to create high-quality visualizations, including 3D plots and interactive charts, helping users interpret complex data.

Advantages and Disadvantages:

Advantages	Disadvantages
MATLAB’s high-quality data visualization tools make it ideal for creating complex plots and 3D visualizations.	MATLAB requires a commercial license, making it expensive for individual users or small organizations.
MATLAB offers specialized toolboxes for various applications, including machine learning, signal processing, and control systems.	MATLAB is not open source, limiting access to third-party libraries and restricting customization.
The interactive environment of MATLAB allows for rapid prototyping and easy testing of code and algorithms.	MATLAB can be memory-intensive when handling large datasets, potentially slowing down performance.

Also Read: Top 29 MATLAB Projects to Try in 2025 [Source Code Included]

11. C/C++

C and C++ are closely related, high-performance programming languages. While C is primarily used for system-level programming, C++ builds upon C by adding object-oriented features. These languages are crucial for building optimized, high-speed data processing systems, algorithms, and software applications that demand computational efficiency.

Real-World Applications:

Algorithm Optimization: C and C++ are frequently used to implement performance-critical algorithms, especially in applications that require real-time processing, such as gaming engines or financial systems.
Data Processing: These languages are ideal for building efficient data processing systems capable of handling large volumes of data with minimal delay.
Machine Learning Libraries: Many popular machine learning libraries, such as TensorFlow and Caffe, have their core implementations written in C or C++ to ensure fast and efficient execution.

Advantages and Disadvantages:

Advantages	Disadvantages
C and C++ are among the fastest programming languages, ideal for high-performance applications and real-time processing.	C and C++ have a complex syntax, making them more challenging for beginners to learn and master.
Their ability to manually manage memory allows for fine-tuned memory usage, optimizing performance in data-intensive applications.	Manual memory management can lead to memory leaks and segmentation faults if not handled carefully.
C and C++ are widely used in systems programming and hardware-related software, making them essential for data science tasks involving real-time processing.	C and C++ are not ideal for data visualization, limiting their use in tasks that require graphical representation of data.

Also Read: 12 Essential Features of C++: Understanding Its Strengths and Challenges in 2025

12. Swift

Swift is a high-performance programming language developed by Apple for iOS, macOS, watchOS, and TVOS applications. While primarily used for mobile and desktop apps, it is gaining popularity in data science due to its speed, safety, and ease of use. With frameworks like CoreML and Swift for TensorFlow, Swift is becoming a powerful tool for machine learning, data visualization, and scalable data solutions in modern data-driven applications.

Real-World Applications:

Machine Learning: Swift is widely used in machine learning workflows, particularly with Apple's CoreML framework, allowing developers to integrate machine learning models into mobile applications efficiently.
Data Analysis: With libraries like Swift for TensorFlow, Swift enables the manipulation and analysis of data, making it suitable for a variety of data science tasks.
Data Visualization: Swift can be used with libraries like SwiftPlot to create interactive, high-quality visualizations for data analysis and presentation.

Advantages and Disadvantages:

Advantages	Disadvantages
Swift offers fast execution speeds, making it ideal for data science tasks like machine learning and real-time processing.	Swift’s ecosystem for data science is still developing, with fewer libraries and resources compared to more established languages.
Swift’s type safety helps catch errors at compile time, making code more reliable and secure.	Swift is most effective within the Apple ecosystem, limiting its use for cross-platform data science applications.
Swift’s modern syntax is clean and beginner-friendly, making it easy to read and write code compared to older languages like C++.	While growing, Swift is newer to the data science field, meaning there’s a smaller community and fewer tools available.

Also read: Top 12 Data Science Programming Languages in 2025

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In 2025, Python, R, and SQL dominate the data science industry. Python's rich libraries, R’s statistical power, and SQL's data management capabilities make them essential for data manipulation, analysis, and machine learning. These languages are key to handling large datasets and building robust models.

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Frequently Asked Questions (FAQs)

1. What programming language is used in data science?

Data science programming languages like Python, R, and SQL are essential tools for various tasks. Python is widely favored for its ease of use, flexibility, and strong machine learning libraries. R is primarily used for statistical analysis and data visualization, while SQL, Java, Julia, and Scala are more focused on handling databases, high-performance tasks, and big data processing in data science workflows.

2. Which programming language is best for data analysis?

Python and R are the top data science programming languages for data analysis. Python offers powerful libraries such as Pandas, NumPy, and Matplotlib for data manipulation and visualization. R, on the other hand, is highly specialized in statistical computing and features robust visualization libraries like ggplot2 and dplyr, making it ideal for scientific and academic data analysis.

3. What is data science programming?

Data science programming involves writing code to collect, clean, and analyze large datasets. It includes tasks such as manipulating data, applying machine learning algorithms, and creating data visualizations. Programming in this field is essential for automating workflows, building predictive models, and extracting insights from data, making it a key part of any data-driven project.

4. Is Python or R better for data science?

Python is a more versatile data science programming language widely used for machine learning, automation, and general data analysis. It has an extensive ecosystem of libraries like TensorFlow and Scikit-learn. R, however, excels in statistical analysis and data visualization, often preferred in research and academic environments for its detailed statistical modeling and graphical representation.

5. Which language is most used for data science?

Python is the most commonly used data science programming language due to its ease of use, readability, and extensive library support. Libraries like Pandas, NumPy, and Scikit-learn make it ideal for everything from data cleaning to machine learning. Python’s popularity is also driven by its broad community and constant updates, making it the go-to language for data scientists worldwide.

6. Which coding is best for data science?

The best data science programming language depends on the task at hand. Python excels in machine learning and automation, while R is the top choice for statistical analysis and visualization. SQL is key for querying databases, and Scala is used for big data processing, particularly with tools like Apache Spark. Each language has its specific use case in the data science workflow.

7. Is Java or Python better for data science?

Python is generally preferred for data science programming due to its simplicity, vast libraries, and strong support in machine learning. Java is more suited for big data systems and high-performance applications but lacks the flexibility and ease of use that Python offers for most data science tasks. Python’s extensive ecosystem makes it the go-to for data scientists in most projects

8. Is C++ better than Python for data science?

C++ provides superior performance for computationally intensive tasks, making it ideal for certain high-speed applications. However, Python remains the better choice for data science programming due to its simplicity and rich ecosystem of libraries like NumPy and SciPy. While C++ may be faster, Python is more efficient for most data analysis and machine learning tasks due to its ease of use.

9. Which programming language is best for big data processing?

For big data processing, data science programming languages like Scala, Java, and Python are frequently used. Scala, particularly with Apache Spark, is excellent for handling large-scale data processing. Python, through libraries like PySpark, is also a great option for big data tasks, while Java is widely used in enterprise environments for managing large distributed data systems.

10. Are there any no-code or low-code alternatives for data science?

Yes, several no-code or low-code platforms like KNIME, Alteryx, and Google AutoML allow users to implement data science programming solutions without writing extensive code. These tools are particularly helpful for business analysts or non-technical users to build data models, automate processes, and analyze data without requiring deep coding knowledge, making data science more accessible.

11. Can I use multiple programming languages in a single data science project?

Yes, using multiple data science programming languages in a single project is quite common. For example, Python might be used for data analysis and machine learning, SQL for querying databases, and R for statistical modeling and visualization. This approach takes advantage of the unique strengths of each language, enhancing the project’s efficiency and improving the final outcomes

Rohit Sharma

834 articles published

Rohit Sharma is the Head of Revenue & Programs (International), with over 8 years of experience in business analytics, EdTech, and program management. He holds an M.Tech from IIT Delhi and specializes...

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