π PyWeek
Scientific Programming & Data Analysis Workshop
PyWeek is a 3-day Python-based workshop designed for STEM students entering scientific programming β no prior coding experience required. Over 3 days, learn Python fundamentals, data analysis, and AI-assisted prototyping through hands-on projects and practical challenges.
Duration
3 Days
Format
Online
Level
Beginner
Language
Python
Workshop Curriculum
Day 0 β Environment Setup
π Before May 4 Β· β± 1β2 hours
Prepare your development environment before the workshop. Install Python, essential libraries, and configure your code editor. Verify all installations work correctly.
Day 1 β Python Basics & Scientific Programming Intro
π May 4, 2026 Β· β± 2β3 hours
Learn Python fundamentals β variables, data types, loops, conditionals, functions, dictionaries, and file I/O with CSV. Introduction to NumPy, Pandas, Matplotlib, and SciPy with practical challenges.
Day 2 β Assignment Analysis & AI-Powered Prototyping
π May 6, 2026 Β· β± 2β3 hours
Review homework solutions from Day 1. Learn rapid prototyping with AI tools β ChatGPT, Claude, and Copilot. Master prompt engineering techniques and live demos of effective coding workflows.
Day 3 β Evaluation & Development Roadmap
π May 8, 2026 Β· β± 2β3 hours
Student presentations of their AI-assisted prototypes. Evaluation criteria: functionality, code quality, AI usage, presentation clarity. Open discussion and resources for self-development.
Domain-Specific Challenges
After Day 1, participants will receive domain-specific challenges aligned with their research interests:
Bioinformatics Challenge
Apply Python and data analysis to biological sequence analysis or protein structure prediction.
Neuroscience Challenge
Work with signal processing and neural data analysis using NumPy and SciPy.
What You'll Learn
β Python Fundamentals
Variables, data types, loops, conditionals, functions, and file I/O operations
β Scientific Libraries
NumPy for numerical computing, Pandas for data manipulation, Matplotlib for visualization
β Data Analysis
Read, process, and visualize CSV data; statistical analysis with SciPy
β AI-Assisted Development
Prompt engineering and rapid prototyping with ChatGPT, Claude, and GitHub Copilot
π₯ Resources & Downloads
All practical examples, Jupyter Notebooks, and additional materials are available on the resources page:
π Go to All Resources β