Python Quick Strike : Register for the Cohort! Accelerate your Python proficiency Register for the Cohort! Python Quick-Strike Learning Cohort is offered through a collaboration between Princeton Institute for Computational Science and Engineering (PICSciE) 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.Join the Python Quick-Strike Learning Cohort to accelerate your Python skills and gain the confidence to apply them in your academic, professional, or personal projects. Whether you aim to analyze data, develop machine learning models, or simply improve your programming abilities, this cohort offers the tools and knowledge you need to succeed. Learning Objectives Understand the structure of the Python language, including concepts such as variables, data structures, and objectsLoad data files for analysis using DataFrames in Pandas and arrays in NumpyIdentify common pitfalls which lead to performance slowdowns via critical thinking and with the aid of profiling/debugging toolsGain confidence applying robust Python libraries such as scikit-learn and PyTorch to coding workflows Visualize data using common Python packages such as matplotlib and seaborn while applying best practicesDevelop the ability to effectively parse and utilize module and library documentation to enhance your technical proficiency. Fall 2024 Events Python Quick Strike Learning Cohort: Session 1- Introduction & Setting Up Sep 24, 2024, 5:00 pm Python Quick Strike Learning Cohort: Session 2- Data Structures, objects, classes Oct 1, 2024, 5:00 pm Python Quick Strike Learning Cohort: Session 3- Pandas Oct 8, 2024, 5:00 pm Python Quick Strike Learning Cohort: Session 4- Data Processing & Benchmarking Oct 22, 2024, 5:00 pm Python Quick Strike Learning Cohort: Session 5-Basic scikit-learn Oct 29, 2024, 5:00 pm Python Quick Strike Learning Cohort: Session 6- Basic PyTorch Nov 12, 2024, 5:00 pm Python Quick Strike Learning Cohort: Session 7- Data Visualization Nov 19, 2024, 5:00 pm Python Quick Strike Learning Cohort: session 8- Capstone Presentations and Certificates Dec 3, 2024, 5:00 pm Facilitators and Organizers Caridad Estrada, GS, CEE University Administrative Fellow Sonali Majumdar Assistant Dean for Professional Development Mattie Niznik Research Software & Programming Analyst, PICSciE Program Partner Get Learning Cohort Alerts! Watch the GradFUTURES newsletter for upcoming learning cohort and event announcements, or complete the form below. GradFUTURES Learning Cohort Interest Form About GradFUTURES Learning Cohorts GradFUTURES’ interdisciplinary learning cohorts build community among and between graduate students and reinforce each student’s graduate training while drawing on their content knowledge to inform the cohort’s investigation of the topic. As part of the cohort, students will read and discuss books, articles, and case studies. Learning cohorts typically also include at least one experiential component such as an immersive project, a site visit, conference presentation, or fellowship/internship opportunities. Interdisciplinary discussions, reflection, synthesis, community building, and immersive experiences are integral components of each learning cohort experience.