Course by tag

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.

AI-Aided Systematic Reviewing

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 screen up to 95%(!). This summer school course introduces you to the open-source software ASReview to help you speed up your systematic review.

Data Science: Programming with Python

Python has become the dominant programming language used in data science. This course offers an introduction to computational thinking about data-related problems and the implementation of data analysis programmes with Python. It starts at the very basics and is explicitly intended for students who have no or only little programming experience.

Introduction to Complex Systems

Our world has an abundance of so-called complex systems. These are typically large collections of connected elements that influence each other. In this hybrid course, we combine examples across physics, the life sciences, socio-economic sciences and humanities with an introduction to basic mathematical tools to learn a complex systems way of thinking. The main aim is to show students how complex systems science is applied by Utrecht University researchers to challenging societal problems.

Data Science: Data Analysis

The course Data science: Data Analysis offers a range of techniques and algorithms from statistics, machine learning and data mining to make predictions about future events and to uncover hidden structures in data. The course has a strong practical focus: participants actively learn how to apply these techniques to real data and how to interpret their results. The course covers both classical and modern topics in data analysis.

Data Science: Statistical Programming with R

This course offers an elaborate introduction to statistical programming with R. Students learn to operate R, form pipelines for data analysis, make high quality graphics, fit, assess, and interpret a variety of statistical models, and do advanced statistical programming. The statistical theory in this course covers t-testing, regression models for linear, dichotomous, ordinal, and multivariate data, statistical inference, statistical learning, bootstrapping, and Monte Carlo simulation techniques.

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.

Modeling the Dynamics of Intensive Longitudinal Data

This is a four-day course on how to study dynamics in intensive longitudinal data, such as ambulatory assessments (AA), experience sampling method (ESM) data, ecological momentary assessments (EMA), real time data capture, observational data or electronic daily diaries. We provide a tour of diverse modeling approaches for such data and the philosophies behind them, as well as practical experience with these modeling techniques using different software packages (including R and Mplus).

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 land use and transport datasets.

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, social sciences, and healthcare.

Data Science: Applied Text Mining

This course introduces the basic and advanced concepts and ideas in text mining and natural language processing. In this course, students will learn how to apply text mining methods on text data and analyse them in a pipeline with machine learning and deep learning algorithms. The course has a strongly practical hands-on focus, and students will gain experience in using text mining on real data from social sciences, humanities, and healthcare and interpreting the results.

Research design for Experimental Linguists - Uil OTS

This course is essential for experimental linguists. In this course you will learn about important aspects of a quantitative study design (research methodology), the basics of statistical (hypothesis) testing, and how methodology and statistics relate to each other. This discussion-based course will teach you to make funded decisions throughout the research process, and consequently conduct better research with valid and reliable outcomes.

Introduction to Multilevel Analysis

This course will teach you the theoretical basics of multilevel modelling and some important methodological and statistical issues. You will also learn how to analyse multilevel data sets with the HLM, Mplus and R programmes, to interpret the output and to report the results. The benefits of multilevel analysis are discussed both in theory as with empirical examples. This course restricts to a quantitative (i.e. continuous) outcome variable. Categorical outcomes are part of the course Advanced Multilevel.

Survey Research: Statistical Analysis and Estimation

The course is based on a total survey error perspective and discusses the major sources of survey error. Participants will be presented with tools for detection and adjustment of such errors. Analysis methods are introduced using both SPSS and R. Topics include complex sampling, nonresponse adjustment, measurement error, analysis of incomplete data and advanced use of administrative data. Special attention will be given to the analysis of complex surveys that include weighting, stratification and design effects.

Survey Research: Design, Implementation and Data Processing

Changes in technology and society strongly influence modern survey research. This course covers the essentials of modern survey methodology, organized by the Department of Methodology and Statistics in collaboration with Statistics Netherlands (CBS). Central to the course is survey quality and the reduction of Total Survey Error (coverage, sampling, nonresponse, including questionnaire and mode effects), while balancing logistics and survey costs. Best practice guidelines for surveys from design to implementation, analysis and reporting will be discussed.

Applied Multivariate Analysis

The summer school course 'Applied Multivariate Analysis' offers hands-on experience using SPSS for the most frequently encountered multivariate statistical techniques in the social and behavioural sciences. The emphasis is on applying multivariate techniques using the computer programme SPSS, and on how to interpret the SPSS output in substantive terms. During the course, we do not discuss the mathematical details of these techniques.

Advanced Survey Design

This course in ‘advanced survey design’ takes students beyond the introductory courses and will discuss the state of the art in both the design and the analysis of survey data. We discuss new ways to analyse modern surveys, including non-probability survey designs, and surveys conducted via apps. Course participants must be proficient working with the statistical software package R at the level of at least knowing Tidy and multivariate regression in R.

Advanced Multilevel Analysis

This three-day course will teach you advanced topics in multilevel modelling. It  builds upon the contents of the other summer school course “Introduction to multilevel analysis”. It consists of three days with lectures in the morning and computer labs in the afternoon. After taking this course, you should be able to analyse more complex multilevel models and to interpret and report the results.

Hands-on GIS for Earth Scientists

This course is a hands-on course on GIS with two weeks of practicals in the GIS Lab of the faculty of Geosciences. The following topics will be covered: Working with ArcGIS Pro and Spatial Analyst, Modelbuilder and Python, automatic DEM extraction of stereo aerial photographs using Erdas Imagine eATE and Agisoft, Mobile GIS / GPS data collection, local/global datasets and datatypes, and poster making.

Exploring Culture through Data: Digital methods & Data practices

Please note that the dates for this summer school have not been finalized and may thus still be subject to change.

Are you curious, eager to learn and would you like to have an enriching experience during your summer holidays? This course would make a great opportunity! The accelerating datafication of society constitutes challenges and opportunities for humanities research. We welcome you, students and non-students, to join us in this online crash course in data practices and digital methods.