Python Quick Strike Learning Cohort: Session 4- Data Processing & Benchmarking

Session Description: Transforming data into usable and/or convenient forms often ends up taking the majority of the computational time during a project. In this session, we’ll look at some common pitfalls when number-crunching and explore tools that can help identify bottlenecks that, with careful thought, can be sped up so you can work more quickly.

Series Overview: Python Quick-Strike Learning Cohort is offered through a collaboration between Princeton Institute for Computational Science and Engineering (PICsiE) and GradFUTURES. The Python Learning Cohort is an intensive, eight-week program open to all graduate students, postdocs, and research professionals eager to develop practical Python programming skills from the ground up. Whether you’re a complete beginner or looking to solidify your foundational knowledge, this learning cohort offers a structured path to proficiency with topics relevant to all four divisions: Humanities, Social Sciences, Natural Sciences and Engineering. Upon successful completion of learning cohort including capstone, participants will receive a micro-credential and certification recognizing their proficiency in Python.

Throughout this cohort, participants will engage in a series of targeted sessions that systematically build from basic concepts to advanced applications in data science and machine learning. The curriculum is designed to ensure that by the end of the program, you have a comprehensive understanding of Python and can apply your skills in real-world scenarios.