🐍 Introduction to Python for Life Sciences

📝 Course Overview

Python is a versatile programming language widely used in research for data analysis, automation, and computational biology. This course provides a hands-on introduction to Python, covering essential programming concepts with applications in life sciences.

The course is structured as a two-week intensive programme, alternating between live sessions and self-study. Participants will learn Python syntax, data structures, file handling, and basic data visualisation, and apply them to biological datasets.

🚀 This course is the first step in the GS-LS Python learning line. Completing it will prepare you for more advanced Python courses such as Python for Data Analysis and Machine Learning for Research.

🎯 Learning Objectives

• Understand the basics of Python syntax, variables, and loops.
• Learn how to work with lists, dictionaries, and sets.
• Write and use functions and modules for structured programming.
• Handle files and data input/output operations.
• Perform basic data visualisation with Matplotlib.
• Gain confidence in writing Python scripts for life sciences applications.

🗓️ Course Schedule

Session	Topic
1	Introduction, Python setup, Variables & Loops
2	Lists, Dictionaries & Data Structures
3	Functions, Modules & Best Practices
4	File Handling & Working with Data
5	Introduction to Data Visualisation
6	Final Assignment & Project Presentation (Online)

The course takes place over 6 sessions, spread over two weeks. Sessions contain lectures, coding challenges, and interactive exercises. Expect to spend additional time practising Python programming concepts outside of class.

🎓 Final Assessment

To finalise this course, participants must complete a final assignment. You can choose one of two options:

1. Exam-Style Assignment: A structured set of exercises covering key topics. An answer model will be provided for self-assessment.
2. Personal Data Project: Use your newly acquired Python skills on your own research data and present your findings in a Jupyter Notebook. This option allows for deeper application of Python in your research.

The final assignment must be completed to receive a course certificate.

🧰 Prerequisites

• No prior programming experience is required.
• Basic familiarity with data analysis is helpful but not mandatory.

📚 Instructional Method

• Pre-Recorded Lectures: Theory will be available in video format for flexible learning.
• Interactive In-Class Sessions: Live sessions focus on Q&A, coding challenges, and exercises.
• Self-Study & Practice: Participants are expected to spend additional time practising exercises.
• Personalised Support: Teachers will be available for guidance and feedback throughout the course.

💻 Tools and Resources

• Software: Python (via Anaconda) & VSCode (installation instructions provided before the course).
• Course materials: Jupyter notebooks, Python scripts, and example datasets will be shared.
• Additional resources: Cheat sheets, recommended readings, and video tutorials.

⭐ What Participants Say

✅ Clear explanations & engaging teaching style – Survey respondents highlighted that the instructor is well-prepared, explains concepts clearly, and keeps the sessions engaging.
✅ Great mix of theory and practice – Participants appreciated that the course balances structured lectures with practical coding exercises, helping them build confidence in Python.

💡 *Heads-up: – Some participants mentioned that the course can be fast-paced, especially for those completely new to coding. To help with this, we provide step-by-step guidance, extra resources, and encourage you to practise at your own pace!

🚀 How to Enrol

To sign up, check the registration page or contact me directly at l.w.dijkhuizen@uu.nl.

---

Need more details? Feel free to reach out! 📩