Our world has an abundance of so-called complex systems. These are typically large collections of connected elements that influence each other. In this online 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.
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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.
R is rapidly becoming the standard platform for data analysis. This course offers an elaborate introduction into statistical programming in 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.
If you expect to work with the software Mplus, this course can help you to get started! This course is a compact 1-day workshop on using Mplus to get you started. We will focus on how to get the syntax running; how to avoid common mistakes; how to interpret the output and how to deal with error messages. In the exercises you will run multiple regression and factor analysis models, which are the basis of many structural equation models in Mplus.
This 4-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.
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).
This course focuses on analysis of outdoor movements in space and transport network-based distances. 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 aims to collect, clean and enrich these datasets with GIS by combining locational data with existing land use and transport datasets.
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.
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.
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.
Process mining is a rapidly growing field of research. This is testified by an established research community, numerous publications and thousands of organisations applying process mining. Because of this interest, we now face a vast body of knowledge that spans multiple disciplines and covers a range of methods, tools, and techniques. A single individual may find it daunting to set up a new research line in this field. After all, it is difficult to oversee all relevant developments and to understand the different approaches available. To help and guide aspiring process mining researchers, this course aims at bringing them together.
This is a five-day course on structural equation modeling (SEM) using Mplus. If you already know how to analyse your data in Mplus but want to learn more about what you are actually doing, and especially if you want to know more about advanced longitudinal analyses, this course is for you. The course consists of in-depth lectures on the fundamentals of Mplus and advanced longitudinal models.