While Python has been the most popular programming language since 2019, data scientists often critique its slow speed and limited capabilities in handling big data scenarios. In this workshop series, we’ll tackle how to enhance Python’s performance in data science by diving deep into its workings and leveraging technologies to transform Python into an effective tool for high-performance big data analytics.
In this quarter, we’ll concentrate on significantly accelerating Python code at the interpreter level. We’ll demystify concepts such as the GIL and JIT and introduce packages like PyPy, Numba, Pythran, and Cython. While there are no strict prerequisites for this lecture, prior programming experience in Python will be advantageous for a complete understanding of the material.