online course - INTRODUCTION TO PYTHON PROGRAMMING FOR BIOLOGISTS - 24-27 February

[info--- via Python-list]

Dear all,
there are the last seats available for our online course - INTRODUCTION TO 
PYTHON PROGRAMMING FOR BIOLOGISTS
Dates: 24–27 February
Course Website: [ https://www.physalia-courses.org/courses-workshops/python24/ 
]( https://www.physalia-courses.org/courses-workshops/python24/ )
This four-day course is tailored for biologists and life scientists at all 
levels with little or no prior programming experience. Participants will gain a 
strong foundation in Python programming and its applications in biological data 
analysis, with a focus on hands-on exercises and real-world projects.
 
By the end of the course, you will be able to:
Write and run Python code for biological data analysis and visualization.

Use bioinformatics libraries like BioPython.

Perform statistical analysis with Python.

Create organized and reusable code.

Develop a simple project tailored to your research field.
Program Highlights:
 
Day 1: Python basics, data types, and DNA sequence manipulation.
Day 2: Dictionaries, loops, and field data analysis.
Day 3: I/O handling, BioPython, and ChIP-seq data processing.
Day 4: Statistics, visualization, and advanced projects in viral genomics.
 
Best regards,
Carlo
 
 



Carlo Pecoraro, Ph.D


Physalia-courses DIRECTOR

i...@physalia-courses.org

mobile: +49 17645230846




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Machine Learning Methods for Longitudinal Data with Python – Online Course (6-9 May)

[info--- via Python-list]

Dear all,
There are still 5 seats left for the upcoming Physalia course "Machine Learning 
Methods for Longitudinal Data with Python," which is taking place online from 
6-9 May. This course will provide a comprehensive introduction to analyzing 
sequence data (repeated over time or space) when time and causation play a 
crucial role.
 
This course will cover both classical statistical and modern machine learning 
approaches to handling time-dependent data. Participants will learn how to 
recognize and address temporal dependencies, disentangle cause-effect 
relationships, and apply appropriate modeling techniques for forecasting, 
survival analysis, and multi-omics data integration. Topics will include:
Statistical and machine learning methods for sequence data
Bias resolution: confounding, colliding, and mediator biases
Time-series forecasting and predictive modeling
Bayesian networks and graph models
Applications in epidemiology, gene expression, and multi-omics
The course combines lectures, hands-on exercises, and case studies to ensure 
participants gain practical skills for applying these methods to real-world 
biological data.
 
 
To register or learn more, please visit [ 
https://www.physalia-courses.org/courses-workshops/longitudinal-data/ ]( 
https://www.physalia-courses.org/courses-workshops/longitudinal-data/ )
 
Best regards,
Carlo
 
 
 



Carlo Pecoraro, Ph.D


Physalia-courses DIRECTOR

i...@physalia-courses.org

mobile: +49 17645230846

[ Bluesky ]( https://bsky.app/profile/physaliacourses.bsky.social ) [ Linkedin 
]( https://www.linkedin.com/in/physalia-courses-a64418127/ )


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