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.
10 course(s) found
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…
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AI-Aided Systematic Reviewing
Online course
More and more researchers rely upon Systematic Reviews: attempts to synthesize the state of the art in a particular scientific field. However, the scientific output of the world doubles every nine years. In this tsunami of new knowledge, there is not enough time to read everything – resulting in costly, abandoned or error-prone work. Using the latest methods from the field of Artificial Intelligence (AI), you can reduce the number of papers to…
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
Introduction to Structural Equation Modeling using lavaan (E-Learning Course)
In this e-learning course, we will cover the basics of structural equation modeling (SEM) using the R package lavaan. Participants will learn how to interact with the lavaan software and how to run common types of SEMs (e.g., path models, confirmatory factor analyses, latent regression models, multiple group models) using lavaan. No prior knowledge of lavaan is necessary, but some experience with R and SEM (although not strictly required) is…
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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Network Science
How can networks help us understand and predict social systems? How to find important individuals and communities? How to predict unobserved connections between genes? How to learn the dependencies between interrelated entities? How can we stop disease spreading in networks? In this course, we provide participants with the conceptual and practical skills necessary to use network science tools to answer social, economic and biological questions.
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Regression in R
Linear regression is one of the most ubiquitous statistical methods. Most statistical techniques can be viewed as either special cases of linear regression (e.g., t-tests, ANOVA) or generalisations of linear regression (e.g., multilevel modelling, SEM, neural networks, GLM, survival analysis). In this course, students will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor…
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Missing Data in R
Missing data are ubiquitous in nearly every data analytic enterprise. Simple ad-hoc techniques for dealing with missing values such as deleting incomplete cases or replacing missing values with the item mean can cause a host of (hidden) problems. In this workshop, we will discuss principled methods for treating missing data and how to apply these methods in R. We will cover basic missing data theory, methods for exploring/quantifying the extent…
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Introduction to R
This workshop will introduce students to the R statistical programming language. R is a completely free and open-source programming language and environment for statistical analysis. In this course, students will learn what R is and how it differs from other statistical software packages and programming languages. They will learn the basics of data I/O, manipulation, and visualisation in R. We will also cover basic statistical analyses such as t…
1
Text Analysis with Python
This course introduces the basic concepts of text analysis in Python. In this course, participants will learn how to apply text mining methods on text data and analyse them in a pipeline with machine learning and natural language processing algorithms. The course has a strongly practical hands-on focus, and participants will gain experience in using text mining on real data.
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