C/C++: High-performance languages, ideal for low-level data manipulation and computationally intensive tasks, but require deeper programming knowledge.
Python: Undisputed champion. Easy to learn, vast libraries like NumPy, Pandas, Scikit-learn, and TensorFlow make it versatile for various analysis tasks.
R: Statistical powerhouse. Ideal for complex statistical modeling, graphics, and interactive data exploration. Great for academic research and bioinformatics.
SQL: The language of databases. Essential for querying and manipulating data stored in relational databases. A must-have for any data analyst.
Julia: Speed and versatility. Rising star known for its performance in scientific computing and machine learning. Requires some learning investment but offers significant speed advantages.
Scala: Big data slayer. Designed for distributed computing and excels in handling large datasets with frameworks like Spark.
Java: Widely used in enterprise applications, offers powerful data analysis libraries like Apache Spark and Hadoop.
Kotlin: Modern language gaining traction, particularly for Android development. Offers libraries like Koin for data analysis tasks.
C/C++: High-performance languages, ideal for low-level data manipulation and computationally intensive tasks, but require deeper programming knowledge.