Life Sciences Courses
The Utrecht Summer School offers a variety of academic courses in the fields of Medicine; Veterinary Medicine; Pharmacology; Global, Maternal and Child Health; and Epidemiology.
8 course(s) found
Biostatistics for Researchers
The course provides an introduction into statistical methodology for life sciences and it discusses a number of statistical techniques for practical data analysis. Concrete examples and case studies are used to apply the theory to practical situations. The statistical techniques covered are several t-tests, chi‐square tests, analysis of variance (ANOVA), (multiple) linear and logistic regression and survival analysis. The course ends with a…
Molecular Epidemiology of the Exposome and Chronic Disease
This course will provide students an introduction into using molecular epidemiology to study the exposome. There is a specific emphasis on the use of molecular markers to characterize relationships between exposures to environmental factors and chronic diseases.
Global Maternal, Newborn and Child Health
This summer course will provide a comprehensive overview of maternal, newborn and child health globally. With an enthusiastic team of experts from all over the world, we will discuss the major challenges which women and children in the world are facing, and we will look at potential solutions. This programme is a collaboration between the University Medical Center Utrecht (Julius Global Health, Wilhemina Children's Hospital), Amsterdam Centre…
3D Printing and Biofabrication
Additive manufacturing (3D printing) uses a layer-by-layer principle for manufacturing. This one-week course will provide an insight into the opportunities of additive manufacturing technologies and 3D (bio)printing in biomedical applications. It will deliver the basics of 3D printing, including hands-on workshops with 3D printers, in which the students will interact with experts demonstrating the working principles and applications of multiple…
Survival Analysis
A one-week course in survival analysis for the life sciences. Concrete examples and case studies are used to apply the theory to practical situations. Survival data, or more generally, time-to-event data (where the “event” can be death, disease, recovery, relapse or another outcome), is frequently encountered in the biomedical sciences. Censoring is a problem characteristic to most survival data, and requires special data analytic techniques.
Data Science in Clinical Practice
Careful data curation and analyses are essential in developing machine learning algorithms, that may usefully contribute to solving problems encountered in routine healthcare. Nevertheless, many valuable contributions never transition from the computer to the bedside. Often implementation is never attempted, or they fail to get the relevant CE marking (or equivalent local standard), or their implementation fails to elicit the intended health…
Data Stewardship
Did you ever wonder how you can improve your data management and handling? Have you ever hoped for a clean and indisputable database that you could easily share with your collaborators? Understand the art of Data Stewardship and get a handle on your data! Data could yield great value when processed intelligently for medical data science but holds great risks when processing is lost in complexity. This online Data Stewardship course will guide…
Collaborative Data Science
Data science relies on working across datasets, teams, disciplines and geographies. Collaboration is crucial, as well as key frameworks. In order to be translatable to patient care, the learning health systems framework helps to conceptualize where healthcare data sits in science, care and evidence domains. Knowledge of key competencies and professions in informatics and data science will facilitate team working. Moreover, without awareness of…