Advanced Python Workshop: Essential Techniques in Data Analysis and Code Management for Research Projects

  • Beginn: 24.02.2025
  • Ende: 09.04.2025
  • Ort: online only
Advanced Python Workshop: Essential Techniques in Data Analysis and Code Management for Research Projects

This 21-hour advanced workshop focuses on essential computational skills for managing research projects as they grow beyond the single-script, single-data-file scale. Intended for students and postdocs with data analysis experience in Python, Matlab, or R, this course uses Python to teach computational skills used by all experimental researchers, including version control using Git, collaboration processes for open science with GitHub, data management techniques like filename convention standards and formats (including HDF5), modular coding techniques, in-process batch processing techniques with loops and control flow, out-of-process batch processing with workflow management using Snakemake, and how to package your project and its dependencies for publication. Overall, this workshop is an excellent opportunity for graduate students and postdocs to enhance their computational skills, thereby significantly contributing to the efficiency and effectiveness of their research endeavors.

About the Trainer: A group leader for the iBehave Open Technology Support group at the University of Bonn, Dr. Nicholas Del Grosso has been working with and teaching neuroscience researchers in Germany computational skills in Python for 11 years. His "Learner-Centered Teaching" philosophy puts socializing, inclusiveness, practice, and applied work at the heart of his hands-on workshops, making them a rewarding and intense experiences for the more-than-700 researchers who have participated in his events. With a Ph.D. in Systems Neuroscience from LMU Munich and a Masters in Behavioral Neuroscience from the Graduate Training Center in Tübingen, Nick is excited to spend time with his alma mater and share some of the latest tools with researchers in Tübingen!


Topics Covered:
Version Control & Collaboration: Dive into Git to master version control, track changes, and collaborate seamlessly, and explore GitHub to manage your code repositories, collaborate with other researchers, and contribute to open-source projects.
Data Management Essentials: Find and process data files efficiently in Python with Glob and Pathlib and discover best practices for filenames to streamline your workflow.
Containerize Scientific Data with HDF5 Files: Unlock the potential of container files for efficient scientific data management.
Modular Code Development: Best Practices with Functions to develop reusable code blocks for efficient programming, including unit testing to increase the reliability and performance of your code.
Efficient Batch Processing: Get comfortable using If-Else, While, and For-Loop Blocks to automate repetitive tasks, save time and reduce errors in batch processing steps in analysis.
Multi-Script Workflow Management: Learn to manage complex, multi-file, multi-language data analysis workflows with ease using command-line interfaces and workflow management frameworks like Snakemake.

Computing Environment Management: Want to make your code easy to install and run anywhere? Want to publish your project according to FAIR principles? We'll look at Conda, Zenodo, and PyPI, and more!

Contact monika.lam@tuebingen.mpg.de for registration information.

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