8 Best Programming Languages for Data Science to Learn in 2025

Summary: Explore the top programming languages for data science in 2025, from Python to SAS. Learn how mastering these tools can help you excel in data analysis and machine learning.

Introduction

The world of data science is booming, and with good reason! Data science helps businesses unlock hidden insights and make informed decisions that can completely change the game. 

But here’s the secret sauce to making data science work: programming languages. These languages are essential tools that allow data scientists to analyse mountains of data and turn it into meaningful information.

Did you know that the Programming Language Market was valued at US$ 188.86 billion in 2023 and is projected to grow to US$ 379.91 billion by 2030? That’s a growth rate of 10.5% annually! 

This gives us a glimpse into how crucial programming languages are in today’s digital economy, especially for fields like data science.

In this blog, we’ll dive into the eight best programming languages for data science that you should learn in 2025. Whether you’re starting your data science journey or looking to upskill, these languages will be your best friends in solving real-world problems.

Key Takeaways

• Python is the most versatile and beginner-friendly programming language for data science.
• R is perfect for deep statistical analysis and data visualization.
• SQL remains essential for managing and querying databases effectively.
• Java and Scala are ideal for big data applications and distributed computing.
• Specialized languages like Julia, MATLAB, and SAS provide advanced capabilities for data analysis and modelling.

Python: The All-Rounder for Data Science

When it comes to data science, Python is the rockstar. It’s the most popular language and the go-to choice for many data scientists. 

Python is like the Swiss Army knife of programming – it’s super simple to learn, has a ton of libraries like NumPy, Pandas, and Matplotlib, and is excellent for data analysis, visualization, and even machine learning.

Why Python is Awesome:

• Simple & Readable: Python’s syntax is beginner-friendly, which makes it easy to read and write.
• Vast Libraries: From data manipulation to machine learning, Python has everything you need.
• Cross-Platform: Python works on Windows, macOS, and Linux, so it’s a flexible tool for all your projects.

R: The Statistician’s Favourite

For all things statistical analysis and data visualization, R is the king! It’s the language that statisticians and researchers swear by. With libraries like ggplot2 for beautiful visualizations and dplyr for fast data manipulation, R is perfect for anyone working on complex data models.

Why R Rocks:

• Statistical Power: R comes with a ton of built-in statistical functions, making it ideal for deep data analysis.
  Great for Visualization: R has some of the best libraries for creating stunning graphs and charts.
• Perfect for Research: R is widely used in academia for data science research, making it great for scientists.

SQL: The Language of Databases

You might think SQL is just for querying databases, but it’s also a vital tool for data scientists. SQL (Structured Query Language) is the backbone of any data-driven project, helping you efficiently extract and manipulate data from databases. Data extraction? SQL has got you covered.

Why SQL is a Must-Know:

• Easy Data Querying: SQL allows you to retrieve data from large databases with ease.
• Data Security: It features built-in controls for managing access and protecting sensitive data.
• Great for Big Data: SQL helps you extract, filter, and analyse data from huge datasets.

Java: The Heavy-Hitter for Big Data

For those looking to work with big data or need scalable performance, Java is the language to learn. With its robust frameworks like Apache Hadoop and Apache Spark, Java excels at processing massive amounts of data. If you’re interested in distributed computing or parallel processing, Java should be on your radar.

Why Java is a Game-Changer:

• High Performance: Java is super fast, which is essential for processing large-scale data.
• Scalable: It’s designed to handle large, complex systems with ease.
• Platform Independent: Java runs on any machine with a Java Virtual Machine (JVM).

Julia: Speed Meets Simplicity

Julia might not be as popular as Python or R, but it’s gaining traction quickly, especially for numerical computing. Julia combines the ease of use found in Python and R, with the performance of low-level languages like C. If you’re working on complex simulations or computationally heavy tasks, Julia is perfect for you.

Why Julia is Perfect for Data Science:

• Blazing Fast: Julia’s performance is top-notch, comparable to C and Fortran.
• Mathematical Focus: It’s designed for numerical and scientific computing, making it ideal for complex calculations.
• Easy to Learn: Julia’s syntax is intuitive and straightforward, so you can start coding right away.

Scala: A Powerful Pairing with Spark

If you’re working with big data and Apache Spark, Scala is your go-to language. Combining both functional and object-oriented programming, Scala is perfect for those looking to write concise, high-performance code. If you want to process large datasets using Spark, Scala is a must-learn.

Why Scala is Super Useful:

• Concise Syntax: Scala’s syntax is clean and reduces boilerplate code.
• Concurrency: It’s great for writing concurrent programs, perfect for handling big data.
• Runs on Java: Scala is compatible with Java so you can take advantage of its vast libraries.

MATLAB: The Tool for Mathematical Modelling

MATLAB is a numerical computing language used extensively in academia and industry. It’s great for data analysis, visualization, and mathematical modelling. If you’re working with simulations or prototypes, MATLAB’s powerful tools and libraries are invaluable.

Why MATLAB is Great for Data Science:

• Interactive Environment: MATLAB offers a user-friendly interface for data visualization and analysis.
• Perfect for Simulation: MATLAB excels at running simulations and solving mathematical problems.
• Strong Community: MATLAB is widely used in both academia and industry, making it easy to find support and resources.

SAS: The Data Science Veteran

Last but not least, SAS (Statistical Analysis System) is a language designed for statistical analysis and data management. It’s widely used in industries like finance, healthcare, and marketing for data manipulation, predictive modelling, and analytics.

Why SAS is Still Relevant:

• Data Integration: SAS easily handles large datasets and integrates data from various sources.
• Advanced Analytics: It’s packed with powerful statistical tools for in-depth analysis.
• Industry Standards: Many large organisations use SAS for business intelligence and data analysis, especially in sectors like finance.

Conclusion: Which Language Should You Learn?

The world of data science is vast, and the programming languages you learn can define your path. While Python is the most versatile and popular choice, languages like R, Java, SQL, and Julia each serve a unique purpose depending on your area of focus. 

As data science continues to evolve, being proficient in at least a few of these languages will give you an edge in the competitive job market.

So, whether you’re just getting started or looking to level up, mastering these programming languages will ensure that you’re equipped to handle the data-driven challenges of the future. 

If you’re looking to get started or upskill in data science, you can join Pickl.AI’s Data Science Courses to gain hands-on experience and expert-led training. Equip yourself with the skills to thrive in this rapidly growing field! Happy coding!

Frequently Asked Questions

Why is Python the best programming language for data science?

Python is the most popular programming language for data science due to its simplicity, extensive libraries like Pandas and Matplotlib, and its compatibility with machine learning frameworks. It’s a one-stop solution for data analysis, visualization, and more.

What role does SQL play in data science?

SQL is crucial for data science as it allows you to query and manipulate databases. With SQL, data scientists can efficiently extract large datasets, making it indispensable for working with structured data and large-scale data analysis projects.

How is R different from Python in data science?

R is especially powerful for statistical analysis and data visualization. It’s a go-to tool for statisticians and researchers. While Python is versatile, R excels in complex statistical models and visualizing data with libraries like ggplot2.