Programming workshops

Learn to conduct reproducible research data analysis in R and RStudio without prior R experience. Lectures, live coding and practical exercises introduce vectors, matrices, data frames, lists, packages and functions. Participants import and inspect data, handle missing values, subset and join datasets, calculate summaries and export results. Further sessions develop ggplot2 visualisations, R Markdown reports, project organisation, reusable functions and strategies for finding help and troubleshooting errors. Exercises can use supplied datasets or participants’ own research data. 2–6 November 2026, daily 14:00–16:30 and 17:30–20:00, Europe/Madrid (CET, UTC+1); instructor questions 20:00–21:00. Total course commitment: 35 hours. English; 16 places. Regular fee EUR 570, or EUR 456 for Ambassador Institutions. Live attendance is required for the certificate. Payment follows acceptance; advertised fees include VAT.

This practical introduction develops reproducible scientific workflows using Nextflow. Participants learn channels, processes, operators, executors, configuration, parameters and reusable modules, then apply them to an RNA-seq pipeline. The course shows how containerized dependencies and the dataflow programming model support execution on local machines, clusters and cloud infrastructure. Familiarity with Linux or macOS and a command shell is strongly recommended. Online sessions run 09:30–13:30 CET and the listed fee is €380.

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.

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.

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.

Build practical skills for organizing and querying relational data with SQL and SQLite. The course introduces database tables, keys and relationships before working through table creation, record changes, filtering, missing values, aggregations and joins. Exercises also cover dates, categories, subqueries and common table expressions; time permitting, participants explore window functions and SQL within reproducible R workflows. No previous SQL or R knowledge is required, and participants receive datasets, setup instructions, code and exercise solutions. Sessions on 8, 15, 22 and 29 January 2027, 11:30–15:00 Madrid time (CET). Fourteen live hours plus eight hours of independent exercises. Current fee including VAT: EUR 573, or EUR 458.40 for Ambassador Institutions. Limited to 15 participants. Acceptance is confirmed before payment. Delivered live online in English with practical exercises. Live attendance is required for a certificate.

Practise supervising an AI coding agent in reproducible bioinformatics analyses. The workshop begins with requests for individual Python functions in chat, then develops multi-step workflows using Claude's desktop tools on local project folders. Participants learn to inspect and correct generated code, document analyses, and distinguish successful execution from scientifically valid methods and assumptions. Genomics and tabular-data exercises cover delegation, review and reproducibility. Live online, 26–27 January, 14:00–18:00 Europe/Berlin (CET). Fee €280. Requires basic Python, a computer on which software can be installed, the Claude desktop app and an active paid Claude Pro-or-higher subscription for the course.

Develop practical Python skills for collecting, processing and visualising biological data, then train and evaluate machine-learning models using public datasets. Teaching covers APIs, reproducible preprocessing, statistical analysis, neural networks, transformers and potential uses of large language models in life science. Mentored small-group projects apply the methods to biological questions. The course particularly suits PhD students and early-career researchers with beginner Python; previous machine-learning experience is unnecessary. There are 30 selected places, costing £975 for academia or £1,275 for industry. Applications open 30 November 2026 and close at 23:59 UK time on 28 February 2027, with decisions by 15 March.