Data Science workshops

Turn multispectral, hyperspectral and lidar observations into analysis-ready datasets with R. Using forest and ecological examples, participants assess metadata and data quality, correct geometric and radiometric errors, reduce noise, and derive spectral indices and structural features. Exercises connect temporal change analysis and classification with scale, accuracy and uncertainty, producing reproducible workflows for spatial research. 2026-11-10–2026-11-13, 12:00–16:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €420. Participants need basic competence and confidence in R and RStudio; prior remote-sensing experience is optional.

This hands-on course analyses mass-spectrometry measurements of biochemically fractionated cells to infer subcellular protein locations. Participants build reproducible R/Bioconductor workflows for preprocessing, quality assessment, annotation and machine-learning-based localization prediction. Guided examples connect protein profiling measurements to cellular maps and interpretation. The course targets proteomics researchers, bioinformaticians and data analysts; previous proteomics experience is helpful but introductory experimental context is provided. It covers fractionation-based measurements rather than antibody imaging or other spatial microscopy technologies. The dated programme lists online teaching from 12:00 to 16:00 Berlin time on November 10–12.

The second M4H workshop brings mathematics, data science and medical research together to improve care. Mathematical models, numerical methods, statistics and machine learning support precision medicine, healthcare-resource allocation and multimodal patient-data analysis. Keynotes examine evolutionary cancer treatment and model-driven digital twins, longitudinal health records, topology and geometry for cyclic gene expression, statistical approaches to individual mental-health variation and patient-specific blood-flow simulation. Panels connect these methods with cardiovascular medicine, health data science and sustainable brain health. The Department of Mathematics at Politecnico di Milano hosts the in-person workshop on 11–13 November 2026 in Aula Rogers, Building 11, Via Ampère 2, Milan. Posters use Aula Vetrata, Trifoglio Building 13, Via Bonardi 9. The programme runs 11 November 14:00–19:30, 12 November 09:00–18:10 and 13 November 09:00–16:00. Times are Europe/Rome, CET (UTC+01:00). Registration is free and open until 15 October through the form on the official workshop page. Speaking slots are invitation only; general attendance is open, and participants may submit poster abstracts.

This workshop examines practical implementations and evaluation of autonomous machine intelligence in data mining. Themes include predictive world models, joint-embedding predictive architectures, self-supervised multimodal learning, intrinsic motivation and scalable analytics systems. It also considers interpretability, robustness and responsible deployment. The programme combines keynotes, research presentations and a discussion of the gap between architectural ideas and real-world systems. Workshop W05 runs from 08:30 to 12:00 local time. Paper submissions closed on 20 August and camera-ready papers were due on 2 October 2026.

This course develops a metabolomics investigation from experimental design through data preparation and statistical interpretation. Lectures, practical work and group discussion connect analytical chemistry and data collection with reproducible analysis. Exercises cover R data handling and visualisation, xcms processing of untargeted mass-spectrometry data, quality control, missing values and compound annotation. The final module compares univariate tests, multiple-testing correction, principal-component methods, partial least squares and random forests for metabolomic matrices. Sessions: 16, 17, 18 and 20 November, 09:30–13:30, Europe/Berlin (CET, UTC+01:00); no session on 19 November. Live online training; fee €480. Familiarity with R is assumed; prior metabolomics experience is useful.

A five-day practical course for experimentalists and computational biologists analysing single-cell RNA sequencing. Lectures and exercises follow data from sequencing files to expression matrices, quality control, normalisation, batch correction, dimensionality reduction, clustering, annotation and differential expression. Later sessions cover trajectory and pseudotime inference, RNA velocity, single-cell multi-omics integration and functional analysis. Participants finish with an independent dataset analysis and presentation. Bash and R/Bioconductor support reproducible workflows and interpretation. Online, 16–20 November 2026, 14:00–20:00 CET (Europe/Berlin) each day. Fee: €530. Participants need practical Unix command-line skills, R programming experience and familiarity with next-generation sequencing analysis.

Build and evaluate deep-learning models for biological prediction using Python and Jupyter notebooks. Lectures and collaborative exercises develop convolutional networks for classification, regression and image segmentation, with attention to preprocessing, augmentation, class imbalance, cross-validation and overfitting. Later sessions explore learning-rate and training-duration choices, recurrent networks and applications to omics data. Participants interpret model performance and discuss their own research problems. 2026-11-23–2026-11-27, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €530. A biology background is expected; basic Python and Linux familiarity is helpful but not required.

Build reproducible maps and spatial analyses of biodiversity and environmental data using R’s terra package. Practical work covers vector and raster data, projections, thematic and interactive maps, spatial queries, geometry operations and layer transformations. Participants calculate distances, buffers and home ranges, examine terrain and spatial autocorrelation, and combine raster and vector information through extraction, interpolation, map algebra and zonal statistics. Demonstrations and supervised exercises support automated workflows for research datasets; participants may bring their own spatial data. 23–27 November 2026, daily 15:00–18:00, Europe/Madrid (CET, UTC+1). Fifteen live hours plus 20 hours of exercises; instructor consultations 09:00–21:00. Spoken Spanish with English slides and scripts; prior R experience required. Sixteen places. Regular fee EUR 485, or EUR 388 for Ambassador Institutions. Live attendance is required for the certificate. Payment follows acceptance; advertised fees include VAT.

Create interactive, reproducible views of omics datasets with the iSEE Bioconductor framework. Participants configure linked panels, inspect bulk and single-cell RNA data and preserve exploratory workflows. Guided cookbook exercises progress to independent exploration, collections of datasets and participants’ own projects, with troubleshooting and companion-package demonstrations. The framework also supports spatial and proteomic data represented by SummarizedExperiment objects. Sessions: 25–26 November, 13:00–17:00 daily, Europe/Berlin (CET, UTC+01:00). Live online training; fee €280. For advanced MSc, doctoral and postdoctoral researchers. Familiarity with sequencing data, common single-cell data containers and visualisation is helpful.

Develop Bayesian analyses of biological data from prior specification and posterior estimation to computational diagnostics and multimodal integration. Practical work covers conjugate and non-conjugate models, Gibbs and Hamiltonian Monte Carlo, convergence checks, hierarchical regression, regularisation and variational inference. Later sessions compare data-fusion strategies and Bayesian factor models while quantifying uncertainty in integrated biomedical datasets. Sessions: 30 November–4 December, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00), with Slack discussion between meetings. Live online training; fee €480. For advanced MSc, doctoral and postdoctoral researchers with R/Bioconductor, probability, statistics and regression experience.

Apply machine learning throughout drug discovery, from biological target identification to molecular screening. The workshop explains model design, training, evaluation and prediction interpretation, then develops deep-learning and graph-neural-network methods in PyTorch. Practical work turns molecular and omics data into model inputs, implements a graph model from a scientific paper, and examines disease classification and target discovery. The final day builds pipelines from research databases and introduces language-model fine-tuning using biological data. Live online, 30 November–3 December, 14:00–19:00 Europe/Berlin (CET). Fee €480. Aimed at MSc/PhD students and early-career scientists, with basic Python, molecular biology, chemistry and data-analysis knowledge. Previous machine-learning experience is unnecessary.

A practical introduction to AI agents for biological research, covering tool and model selection, context design, costs and privacy. Participants work with research data to produce analyses, figures, presentations and interactive omics portals. Further sessions develop reusable skills, project instructions and agents, connect external knowledge through retrieval and MCP, and assemble reproducible research workflows. The course includes critical review of generated code, limitations, responsible use and laboratory AI policies. Sessions run 14:00–17:00 CET on 3 and 4 December 2026. Intended for life scientists, bioinformaticians and data scientists with basic command-line knowledge and one programming language; previous AI experience is unnecessary. Fee: €350.

This workshop connects genomic discovery with prediction of epilepsy outcomes. It examines genome-based multi-omics in multigenerational cohorts, multimodal deep learning that combines clinical and genomic information to predict antiseizure medication response, and polygenic risk scores integrated with machine learning to estimate disease severity and seizure control. The presentations link these complementary approaches to the problem of clinical heterogeneity and assess their practical role in more individualized treatment. Sunday 6 December, 14:00–15:30 Mountain Standard Time (America/Denver). Part of the 2026 American Epilepsy Society Annual Meeting. In-person meeting registration and a free myAES account are required. Standard full-meeting rates are USD 800 for member scientists and USD 1,075 for non-member scientists; student rates are USD 250 and USD 360, respectively. Other professional and one-day categories are available. Seating priority goes to registered attendees.

This practical course introduces Git and GitHub for scientific work. Lectures and exercises develop approaches to organizing research code, tracking changes and collaborating, connecting version control with transparent and reproducible computational projects.

Develop reusable R workflows for collecting research data from public websites. Learn HTML structure and selectors, extract relevant text and tables with R packages, and clean, transform and export the results into analysis-ready datasets. Demonstrations, live coding, quizzes and individual or group exercises develop adaptable scripts. The course prioritises official APIs and addresses scraping when no suitable API exists, including respect for terms of service, copyright and server load. 10–11 December 2026, daily 16:00–19:30, Europe/Madrid (CET, UTC+1). Seven live hours plus three hours of independent exercises. English; working knowledge of R required, with R, RStudio and relevant packages installed. Fifteen places. Regular fee EUR 320, or EUR 256 for Ambassador Institutions. Live attendance is required for the certificate. Payment follows acceptance; advertised fees include VAT.

AIDaR studies the properties that make scientific datasets useful to foundation models, retrieval tools and agents. The workshop emphasizes links between observations, samples, assays, protocols and analysis outputs so that results can be interpreted beyond their original laboratory context. Session: 08:30–18:15 (Europe/Paris). A paid NeurIPS virtual pass includes workshop livestreams; on-site access follows the host-location registration rules.

The workshop treats trained models and their artefacts as data. Researchers examine weights, gradients, representations and optimization histories across model populations, seeking methods to compare, search, predict and modify model behaviour and understand relationships among models. Session: 08:30–18:30 (Europe/Paris). A paid NeurIPS virtual pass includes workshop livestreams; on-site access follows the host-location registration rules.

Create and interpret visualisations for expression, genomic and comparative-genomic studies in R. The workshop starts with volcano plots, dimensionality-reduction displays, genomic peaks, pathways and heatmaps. It then covers genome coverage, alignments, Manhattan plots and temporal allele-frequency changes, followed by phylogenetic trees, set intersections, circos displays, synteny, ideograms and pangenome graphs. Practical work connects each display to the biological structure of the data. Live online, 14–16 December, 13:00–18:00 Europe/Berlin (CET). Fee €420. Familiarity with R is expected; a short introductory R resource is provided.

This practical course develops intermediate and advanced Python skills for biological data science. Coding exercises connect programming concepts with the stages of a data-analysis workflow, supporting participants who already have basic familiarity with Python.

Learn to build and evaluate machine-learning workflows in R with tidymodels and tidyverse. The workshop moves from problem definition, dataset splitting and preprocessing to decision trees, random forests and gradient boosting. Practical work covers cross-validation, hyperparameter tuning, regression and clustering, with model-building demonstrations, live coding, quizzes and individual or group exercises. Participants should already be comfortable with R, basic data analysis and statistics; machine-learning experience is not required. Sessions on 8, 15, 22 and 29 January 2027, 16:00–19:30 Madrid time (CET). Fourteen live hours plus eight hours of independent exercises. Fee including VAT: EUR 546 through 31 October 2026, then EUR 610; Ambassador Institution rates EUR 436.80 and EUR 488. Limited to 15 participants. Acceptance is confirmed before payment. Delivered live online in English with practical exercises. Live attendance is required for a certificate.