Data science is an interdisciplinary field that relies on a variety of tools and programming languages to manipulate, analyze, and visualize data. The choice of programming language often depends on the specific task or problem at hand. Some languages are better suited for data manipulation, while others excel at statistical analysis, machine learning, or data visualization. In 2025, these are the best programming languages for data science:
1. Python
- Why It’s Popular: Python is the most widely used programming language in data science. It’s user-friendly, versatile, and has an extensive ecosystem of libraries that make data manipulation, analysis, machine learning, and visualization easy.
- Key Libraries:
- Pandas: Data manipulation and analysis.
- NumPy: Numerical computing.
- Scikit-learn: Machine learning algorithms.
- TensorFlow & PyTorch: Deep learning frameworks.
- Matplotlib & Seaborn: Data visualization.
- Use Cases: Data wrangling, machine learning, deep learning, data visualization, automation.
2. R
- Why It’s Popular: R is highly favored in academia and research due to its powerful statistical analysis capabilities. It is also great for visualizing complex datasets.
- Key Libraries:
- ggplot2: Data visualization.
- dplyr & tidyr: Data manipulation.
- caret: Machine learning.
- Shiny: Interactive web apps for data visualization.
- Use Cases: Statistical analysis, data visualization, exploratory data analysis (EDA), bioinformatics.
3. SQL (Structured Query Language)
- Why It’s Popular: SQL is essential for working with relational databases, which are commonly used to store large datasets. It’s the standard language for querying databases.
- Key Functions:
- SELECT: Extract data from databases.
- JOIN: Combine data from multiple tables.
- GROUP BY: Aggregate data.
- WHERE: Filter data.
- Use Cases: Data extraction, manipulation, and querying from relational databases. SQL is crucial for any data scientist working with structured data.
4. JavaScript
- Why It’s Popular: JavaScript is essential for creating interactive visualizations and integrating data science models into web applications. It has become increasingly relevant in the data science ecosystem, especially for front-end developers and full-stack data scientists.
- Key Libraries:
- D3.js: Powerful data visualization library.
- TensorFlow.js: TensorFlow for running machine learning models in the browser.
- Plotly.js: Data visualization.
- Use Cases: Interactive data visualizations, integrating data science models into web applications, client-side machine learning.
5. Julia
- Why It’s Popular: Julia is a high-performance programming language that is gaining popularity in data science due to its speed and ability to handle large datasets. It’s a great choice for numerical and scientific computing.
- Key Libraries:
- DataFrames.jl: Data manipulation.
- Plots.jl: Visualization.
- Flux.jl: Machine learning.
- Use Cases: High-performance numerical analysis, machine learning, scientific computing.
6. Scala
- Why It’s Popular: Scala is often used for big data processing, particularly with Apache Spark. It combines functional and object-oriented programming paradigms and is a great choice for handling large-scale data processing tasks.
- Key Libraries:
- Apache Spark: Big data processing.
- Breeze: Numerical processing.
- Spark MLlib: Machine learning.
- Use Cases: Big data processing, machine learning at scale, distributed computing.
7. MATLAB
- Why It’s Popular: MATLAB is widely used for mathematical modeling, simulations, and algorithms. It’s often used in academia and research, especially in fields like engineering, finance, and bioinformatics.
- Key Libraries:
- Statistics and Machine Learning Toolbox: Machine learning algorithms.
- Deep Learning Toolbox: Deep learning models.
- MATLAB Toolboxes: Specialized libraries for various domains like image processing, signal processing, etc.
- Use Cases: Mathematical modeling, simulations, algorithm development, engineering applications.
8. C++
- Why It’s Popular: C++ is primarily used for performance-critical applications. In data science, it’s used for developing highly efficient algorithms, especially in machine learning and AI.
- Key Libraries:
- MLPack: Machine learning library.
- TensorFlow (C++ API): Deep learning framework.
- Use Cases: Performance optimization, implementing high-performance algorithms, building machine learning libraries.
9. Go (Golang)
- Why It’s Popular: Go is known for its simplicity and efficiency in building fast, scalable systems. In the data science world, it’s used for creating backend services that can process large datasets.
- Key Libraries:
- GoLearn: Machine learning library.
- Gonum: Numerical computation.
- Use Cases: Building scalable, high-performance backend systems, working with large datasets in real-time applications.
10. SAS (Statistical Analysis System)
- Why It’s Popular: SAS is a well-established software suite used for advanced analytics, statistical analysis, and data management. It’s widely used in healthcare, finance, and government sectors.
- Key Libraries:
- SAS/STAT: Advanced statistical analysis.
- SAS Visual Analytics: Data visualization.
- Use Cases: Statistical analysis, data management, healthcare analytics, financial analysis.
11. Swift
- Why It’s Popular: Swift is mainly used for iOS app development, but it’s gaining traction in the data science community for its speed and usability. It’s particularly useful for real-time analytics in mobile apps.
- Key Libraries:
- CoreML: Machine learning integration on Apple devices.
- CreateML: Machine learning model training for iOS apps.
- Use Cases: Mobile data science applications, real-time analytics on iOS devices.