{"id":7380,"date":"2025-04-10T15:00:05","date_gmt":"2025-04-10T13:00:05","guid":{"rendered":"https:\/\/blog.rwth-aachen.de\/forschungsdaten\/?p=7380"},"modified":"2025-04-28T12:06:53","modified_gmt":"2025-04-28T10:06:53","slug":"carpentries-python-workshop","status":"publish","type":"post","link":"https:\/\/blog.rwth-aachen.de\/forschungsdaten\/en\/2025\/04\/10\/carpentries-python-workshop\/","title":{"rendered":"Carpentries Python Workshop"},"content":{"rendered":"

\"Carpentries<\/a>

Source: DKZ.2R and NFDI4Microbiota<\/p><\/div><\/p>\n

The Software Carpentries Introduction to Python workshop on March 17 to 18, 2025, at the IT Center of the RWTH Aachen University. It was led by Jonathan Hartman from the Rhine-Ruhr Center for Scientific Data Literacy<\/a> (DKZ.2R) and Catherine Gonzalez from the NFDI4Microbiota<\/a>. This event was specifically designed for graduate students and researchers who are interested in learning Python.<\/p>\n

<\/p>\n

 <\/p>\n

A Collaborative Experience<\/strong><\/span><\/h3>\n

This was more than just a workshop\u2014it was a community-building experience. Participants were encouraged to help one another, fostering a collaborative and supportive learning environment. Using the Carpentries code of conduct, participants were encouraged to use welcoming and inclusive language, be respectful of different viewpoints and experiences to ensure the best possible experience.<\/p>\n

 <\/p>\n

What is Python<\/strong><\/span><\/h3>\n

Python is an interpreted language, meaning you can write code directly and see the results instantly. It’s like giving instructions to your computer to perform tasks. You write code using the Python language, and the computer executes it step by step. Python is a great programming language to get an introduction to coding as it is very logical and in simplistic spoken English.<\/p>\n

 <\/p>\n

Assessing Participant Progress<\/strong><\/span><\/h3>\n

To better understand the participants\u2019 starting knowledge and expectations for the training, we conducted a pre-workshop survey<\/strong>. This helped us gauge their familiarity with Python and identify key areas where they wanted to improve. After the workshop, we followed up with a post-workshop survey<\/strong> to see how their answers had changed, assess their learning progress, and gather feedback on the training experience. This provided valuable insights into the effectiveness of the workshop and areas for future improvement.<\/p>\n

For example, the chart below shows how participants\u2019 self-reported skills and confidence levels evolved throughout the training.<\/p>\n

 <\/p>\n

\"the<\/a>

Source: DKZ.2R and NFDI4Microbiota<\/p><\/div>\n

 <\/p>\n

We also received encouraging feedback from participants:<\/p>\n

“I learned a lot from this workshop. All the staff were very kind and professional. I’d love to join another Python workshop under your organization. Thanks!”<\/em>
\n“Good organization, very well-structured content with understandable examples. I liked the combination of presentations and hands-on exercises.”<\/em><\/p>\n

“All instructors were very engaged and enthusiastic. They provided great examples of how best practices help in day-to-day coding.”<\/em>
\n“The enthusiasm from the instructors made it easier to stay engaged in the tasks.”<\/em><\/p>\n

\u00a0<\/em><\/p>\n

What Participants Learned<\/strong><\/span><\/h3>\n

Over the course of two days, participants explored key programming concepts through hands-on coding exercises. Some of the main topics covered included:<\/p>\n