Course by tag

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 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.

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

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.

Mplus: How to get started? (April)

This course is currently closed. If you are still interested in this course, please send an email to MS.summerschool@uu.nl 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.

Data Science: Multiple Imputation in Practice

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.

Modeling the Dynamics of Intensive Longitudinal Data

NOTE: this course is fully booked! New applicants will be placed on a waiting list. 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 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.

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.

Process Mining Research

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.

Advanced Course on using Mplus

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.

Introduction to Structural Equation Modeling using Mplus

We offer a 5-day course on how to perform basic SEM analyses using Mplus. The main objective of this course is to learn how to analyse several models with Mplus (e.g. path models, multiple group models, mediation and moderation, confirmatory factor analysis, and longitudinal models). No previous knowledge of Mplus is assumed, but prior knowledge of SEM, although not mandatory, will make this course more useful.

A Gentle Introduction to Bayesian Statistics

This course describes the stages involved in Bayesian analysis: specifying the prior and data models, deriving inference, model checking and refinement. We discuss prior and posterior predictive checking, and selecting a technique for sampling from a probability distribution. Other topics discussed are: approximate measurement invariance (a Bayesian method to assess comparability of data), evaluating hypotheses via the Bayes Factor and information criteria, and combining evidence from multiple studies addressing the same research question. Finally, we propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics).  

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 and Mplus programs, 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, organised 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 text data and sensor data generated by modern surveys. Course participants must be proficient working with statistical software (Stata, SPSS or R). Course materials have been developed in R, but most exercises on days 1-3 is in SPSS or STATA.

Advanced Multilevel Analysis

This three day course will teach you advanced topics in multilevel modelling. The three-day course 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.

Data Science and Beyond: Data Assimilation with Elements of Machine Learning

How do meteorologists forecast the weather and climate? Is there a way to predict the profit from a wind farm? These are some of the questions modern science addresses by using data assimilation. Many research institutes and companies (e.g. KNMI, Shell, US-NCAR or UK MetOffice) develop and employ data assimilation and the demand for trained personnel is constantly growing. The school will describe the theoretical foundation of data assimilation together with numerical tutorials, all the way to state-of-the-art methods, including modern machine learning approaches and their combination with data assimilation.

Hands-on GIS for Earth Scientists

This course is a hands-on course on GIS: 2 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

The accelerating datafication of society constitutes challenges and opportunities for humanities research. This course will acquaint you with (methodological) fundamentals of data practices in the Digital humanities. These will include data collection, data preparation, data visualisation, critical data and algorithm studies, network analysis, and an introduction to programming in Python. Besides training these skills, you will work in small teams on a hands-on case. To top it off, guest speakers from several fields will share their experiences with data practices.