Which programming language is popular for data analysis?

• A. Java
• B. MATLAB
• C. Python
• D. R

Answer: (C)

Python is popular for data analysis because it has a comprehensive ecosystem of libraries that cover every step of the workflow. For manipulating data, you have powerful tools like pandas that make it easy to clean, transform, and analyze datasets. For numerical work, numpy provides efficient array operations, while scipy expands into advanced mathematical functions. Visualization is well-supported with matplotlib and seaborn, helping you explore data and communicate findings clearly. When it comes to modeling and machine learning, libraries such as scikit-learn, along with deep learning frameworks like TensorFlow and PyTorch, enable a wide range of approaches without switching languages. Python’s syntax is readable and approachable, which lowers the barrier to experimentation and rapid prototyping. It also plays nicely with data pipelines and production systems, scales with big data tools like Dask or PySpark, and benefits from a large, active community with abundant learning resources. While MATLAB and R have their strengths in specific contexts (numerical computing and statistics, respectively), Python’s versatility and widespread use in real-world data workflows make it the go-to choice for data analysis in many environments.