The most commonly used programming languages in data science include:

Python: Known for its simplicity and vast libraries like NumPy, pandas, scikit-learn, and TensorFlow, Python is the most popular language in data science for data manipulation, machine learning, and deep learning.

R: Ideal for statistical analysis and data visualization, R is widely used for tasks involving data exploration, statistical modeling, and advanced analytics.

SQL: Essential for querying, retrieving, and managing data from relational databases, SQL is foundational in handling structured datasets.

Java: Often used for big data technologies like Hadoop and Spark, Java plays a significant role in scalable and high-performance applications.

Julia: Emerging as a strong competitor, Julia offers high performance for numerical computing and is gaining traction in machine learning and computational tasks.

SAS: Widely used in industries like healthcare and finance, SAS provides powerful tools for statistical analysis and predictive modeling.

Each language has its strengths, and the choice depends on the project's requirements and personal expertise.

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