(Online course): Advanced Python for Data Science and Bioinformatics - 23–26 March
Carlo Pecoraro
Dear all,
We are pleased to announce the upcoming Advanced Python for Data Science and Bioinformatics course, which will take place online from 23–26 March .
To foster international participation, the course will be held fully online and will consist of four half-day sessions (9:00–13:00 Berlin time) combining lectures and hands-on practical exercises.
Course website: https://www.physalia-courses.org/courses-workshops/advanced-python/
This four-day course provides the foundations and typical workflow of data science, with a particular focus on biology and life sciences, using the Python programming language. Throughout the course, participants will learn intermediate to advanced Python concepts and apply them through practical coding exercises relevant to biological data analysis.
The course is designed for biologists and life scientists at all levels who already have basic programming experience in Python (familiarity with syntax, variables, lists, dictionaries, conditionals, loops, and functions).
By the end of the course, participants will be able to:
• Understand the main steps of a data science workflow in biology
• Write Python code for data wrangling and interaction with common bioinformatics formats
• Understand the basics of machine learning using Python
• Apply principles of effective data visualization
• Develop a complete project from data preparation to model evaluation
Programme
Monday (9:00–13:00 Berlin time)
• Intermediate Python: classes, requests, exception handling, tips & tricks
• BioPython: Python for bioinformatics
• Pandas: data wrangling
Tuesday (9:00–13:00 Berlin time)
Data visualisation
• Introduction and theoretical concepts
• Principles of effective visualization and common mistakes
• Hands-on practice with Python plotting libraries (matplotlib and seaborn)
• End-to-end project: data preparation, exploration, and visualization using Pandas, matplotlib, and seaborn
Wednesday (9:00–13:00 Berlin time)
Machine learning
• Introduction and theoretical foundations
• Supervised, unsupervised, and reinforcement learning
• Commonly used algorithms
• Feature engineering and selection
• Train-test split and cross-validation
Thursday (9:00–13:00 Berlin time)
Project Day: Integrated Workflow and Model Evaluation
• Developing a complete project from data preparation to model evaluation
• Combining data wrangling, machine learning, and visualization techniques
• Advanced tips for optimizing Python code
• Final Q&A and project sharing session
If you are interested in strengthening your Python skills for biological data analysis and machine learning, this course will provide practical tools and a structured workflow for real-world data science projects.
Please feel free to share this announcement with colleagues who may be interested.
Best regards,
Carlo
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
in...@physalia-courses.org