PSLS22 Practical Statistics for the Life Sciences
This intermediate level course is one of our Foundations courses. It covers essential statistical concepts and methods for extracting insights from empirical data in the life sciences. The course positions applied statistics, starting from important aspects of experimental design and data...
PSLS22 Practical Statistics for the Life Sciences
https://gtpb.github.io/PSLS22/
http://tess.elixir-uk.org/materials/psls22-practical-statistics-for-the-life-sciences
This intermediate level course is one of our Foundations courses. It covers essential statistical concepts and methods for extracting insights from empirical data in the life sciences. The course positions applied statistics, starting from important aspects of experimental design and data exploration. We then move into statistical modeling and data analysis. We will focus on the link between linear regression and analysis of variance. Together, these methods contribute to the study of General Linear Models. The course also introduces the basics of non-parametric testing, and addresses categorical data analysis and logistic regression.
Lieven Clement
Milan Malfait
Jeroen Gilis
Graduate students
All postgraduates
PSLS20 - Practical Statistics for the Life Sciences (2020)
Training material that covers essential statistical concepts and methods for extracting insights from empirical data in the life sciences.
Keywords: Statistics, General Linear Models
Resource type: Documentation, Exercise, Handout, Scripts
PSLS20 - Practical Statistics for the Life Sciences (2020)
https://gtpb.github.io/PSLS20
http://tess.elixir-uk.org/materials/psls20-practical-statistics-for-the-life-sciences-2020
Training material that covers essential statistical concepts and methods for extracting insights from empirical data in the life sciences.
The Gulbenkian Training Programme in Bioinformatics
Lieven Clement
Jeroen Gilis
Statistics, General Linear Models
Academia/ Research Institution
Industry
Healthcare
Non-Profit Organisation
Introductory image processing on biological images using python.
A jupyter notebook python practical designed to give students a introduction to opening and processing image files derived from biological samples.
Resource type: Jupyter notebook, PDF
Introductory image processing on biological images using python.
https://github.com/dwaithe/model-training/
http://tess.elixir-uk.org/materials/introductory-image-processing-on-biological-images-using-python
A jupyter notebook python practical designed to give students a introduction to opening and processing image files derived from biological samples.
Dominic Waithe
Anatole Chessel
Volker Baecker
Bioimage Analysts
Image Analysts
Computer Vision scientists
bioinformaticians
computational scientists
Biophysicists
Biologists
Microscopists
Python for Biologists
PhD students
