While Python becomes the most popular programming language since 2019, data scientists often have a few common complaints about its slow speed and the limited capabilities of handling the big data scenarios. In this workshop series, we will present an extensive discussion on how to improve the performance of Python in data science by looking under the hood of its language/libraries and using the technologies to make Python a practical solution for high-performance big data analytics.

In the first session, we will focus on how to boost the speed of python code in an interperter level by explaining the concepts (e.g., GIL, GIT) and introducing the packages of pypy, numba, pythran, cython etc. Although no specific prerequisite is required to attend the talk, having programming experience in Python will be helpful to fully understand the lecture content.

Any questions about this workshop can be emailed to huqy@oarc.ucla.edu.

Register here: https://ucla.zoom.us/meeting/register/tJAod-mrrTwjHdYrBwSM9C3rtRTPAkhEBQXc