All Courses
On this page you can find all the courses and tracks offered by the Utrecht Summer School. Detailed information is published on all individual course pages.
4 course(s) found
Sense making of GPS Data in a GIS environment
This course focuses on the application of GIS Tools to analyse outdoor movements of people or animals. The objective is to provide students with insight in the principles of sense making of GPS Data in a GIS environment. Collection of travel/movement data is nowadays made easy through the use of GPS-loggers and Smartphones. This course focusses to how to collect, clean and enrich these datasets with GIS by combining locational data with existing…
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Teaching through English in an international classroom: CLIL and EMI
A professional development course for teachers and lecturers working through English who would like to improve their CLIL/EMI* methodology skills. Participants will develop and improve their understanding, expertise and skills related to CLIL in higher education. Quotes from previous participants: "It was exactly what I was looking for!", "The course was useful for me to practice teaching in English, and to activate students to learn, not just…
1
Data Science: Introduction to Text Mining with R
Applications of text mining are everywhere: social media, web search, advertising, emails, customer service, healthcare, marketing, etc. In this course, students will learn how to apply text mining methods on text data and analyse them in a pipeline with statistical learning algorithms. The course has a strongly practical hands-on focus, and students will gain experience in using and interpreting text mining on data examples from humanities,…
1
Data Science: Multiple Imputation in Practice
This four-day course teaches you the basics in solving your own missing data problems appropriately. Participants will learn how to form imputation models, how to combine data sets, how to model non-response, how to use diagnostics to inspect the imputed values, how to obtain valid inference on incomplete data and how to avoid many of the pitfalls associated with real-life missing data problems.
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