High-Performance Computing with Python 3.x

This course is designed for Python programmers, data analysts, and aspiring data science professionals who want to enhance their skills in high-performance computing using Python. The course covers various aspects of Python programming, including how to speed up numerical computations using NumPy, SciPy, and Cython. It also explores parallelism by leveraging the power of multiprocessing and multithreading in Python.

Additionally, the course focuses on mastering Dask, a tool used to manage large datasets in a distributed environment, and reactive programming techniques for creating robust and responsive applications. The goal is to equip learners with the necessary skills to optimize critical kernel parts using different tools and understand how Numba can optimize their code.

By the end of the course, participants will be able to use Python for high-performance computing projects and implement distributed Python applications. The course is suitable for individuals who have a basic understanding of Python programming and want to expand their skillset to scale their code and improve its performance.

Overall, this course offers a comprehensive guide on using Python for high-performance computing, covering essential concepts and tools that will enable learners to tackle complex computational tasks efficiently.