BioNT Learning Path 1: Computational Biology and Machine Learning Created byRosemary Sheridan|UpdatedagoThe Bio Network for Training (BioNT) is an international consortium of nine partners, including six academic institutions and three small and medium enterprises (SMEs), dedicated to advancing digital skills in the biotechnology and biomedical sectors. The aim of the BioNT consortium is to provide a high-quality training program and community for digital skills relevant to the biotechnology industry and biomedical sector.The goals of the project's training model are to:Provide high-quality courses in two coherently designed curricula: for basic digital skills of staff and job seekers in healthcare and biotechnology, and empowering technological leaders and innovators.Positively impact course participants and their communities, supporting digital skills in SMEs and larger businesses in these sectors.Ensure sustainability beyond the project's duration, empowering individuals as well as their business-specific, sector-specific, language-specific, etc. communities. Learning Path 1: Computational Biology and Machine LearningThis learning path guides learners from programming fundamentals to the application of machine learning on biological data. It is intended for life-science graduates, career-transitioners, and SME employees who wish to build practical computational analysis skills. Learners progress from foundational Python and applied bioinformatics to working with high-performance computing resources, and finally to applied machine learning.Introductory learning materialsCourse 15 June 2026From zero to Hero with Python (BioNT) In this course, you will learn the core concepts of Python programming through hands-on exercises based on training materials from The Carpentries, an international organisation that teaches foundational coding and data skills to researchers Online Basic Course 15 June 2026A practical introduction to bioinformatics and RNA-seq using Galaxy (BioNT) RNA sequencing (RNA-seq) is a widely used method for studying gene expression and understanding how cells respond to different conditions. While generating RNA-seq data has become routine, the real challenge lies in analysing it properly, making informed decisions at each step so that the biological conclusions are robust and meaningful. The process involves several steps, including quality control, read mapping, quantification, normalisation, and differential expression testing. Decisions made during these steps can strongly influence the final results, making a clear and reproducible workflow essential for drawing reliable biological conclusions. Online Up to 16 HoursBasic Course 08 July 2026An Introduction to High Performance Computing (BioNT) Get to know HPC systems - from how they work to how to use them effectively. HPC is shaping many modern research fields, as they rely on clusters to process data efficiently and run large-scale analyses. Online Up to 12 HoursBasic See less materialsAdvanced learning materialsCourse agoApplied Machine Learning for Biological Data (BioNT) Machine learning has become an important tool for analysing biological and genomic data, helping researchers uncover patterns, make predictions, and gain new insights from complex datasets. From identifying cell types to predicting disease outcomes Online Up to 26 HoursIntermediate See less materialsLearning path DetailsWebsite linkBioNT consortiumDigital skill levelBasicIntermediateAdvancedDigital technology / specialisationDigital skillsDigital transformationLog in to comment
Course 15 June 2026From zero to Hero with Python (BioNT) In this course, you will learn the core concepts of Python programming through hands-on exercises based on training materials from The Carpentries, an international organisation that teaches foundational coding and data skills to researchers Online Basic
Course 15 June 2026A practical introduction to bioinformatics and RNA-seq using Galaxy (BioNT) RNA sequencing (RNA-seq) is a widely used method for studying gene expression and understanding how cells respond to different conditions. While generating RNA-seq data has become routine, the real challenge lies in analysing it properly, making informed decisions at each step so that the biological conclusions are robust and meaningful. The process involves several steps, including quality control, read mapping, quantification, normalisation, and differential expression testing. Decisions made during these steps can strongly influence the final results, making a clear and reproducible workflow essential for drawing reliable biological conclusions. Online Up to 16 HoursBasic
Course 08 July 2026An Introduction to High Performance Computing (BioNT) Get to know HPC systems - from how they work to how to use them effectively. HPC is shaping many modern research fields, as they rely on clusters to process data efficiently and run large-scale analyses. Online Up to 12 HoursBasic
Course agoApplied Machine Learning for Biological Data (BioNT) Machine learning has become an important tool for analysing biological and genomic data, helping researchers uncover patterns, make predictions, and gain new insights from complex datasets. From identifying cell types to predicting disease outcomes Online Up to 26 HoursIntermediate
