Python is Losing Its Crown in Data Science - learnhub

Python has been the go-to language for data science for years because of its flexibility, the sheer number of libraries available, and how easy it is to learn.

But new tools and languages are entering the scene that can sometimes do the job better or faster.

Python has some big hitters in its arsenal: Pandas for data manipulation, NumPy for math-heavy operations, and TensorFlow for machine learning.

Data science is no longer a one -size-fits-all field, and we're seeing the emergence of more specialized tools and languages tailored for specific areas within data science.

While Python still covers a broad range of applications, it’s now competing with languages that provide a more customized, efficient experience for specialized tasks.

Julia is gaining popularity in scientific computing and data science projects that demand high-performance computing.

R is still a powerful tool in data science, especially in academia and statistics-heavy fields.

Scala is built for distributed data processing and big data applications.

Rust’s low-level control and memory safety features make it a good option for edge computing and real-time data processing.