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.
Data science relies on working across datasets, teams, disciplines and geographies. Collaboration is crucial, as well as key frameworks. In order to be translatable to patient care, the learning health systems framework helps to conceptualize where healthcare data sits in science, care and evidence domains. Knowledge of key competencies and professions in informatics and data science will facilitate team working. Moreover, without awareness of the national and international datasets which may be available, the 'big picture' benefits of data science cannot be achieved.
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 or information 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.
This course introduces the basic concepts of text analysis in Python. 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 strong practical hands-on focus, and participants will gain experience in using text mining on real data.
Probabilistic graphical models (PGMs) represent the world in a simple way that humans can understand better.
They are graphical representations to understand the complex relationships between a set of random variables of interest (nodes). The edges that connect these nodes show the statistical dependencies between them.
PGMs have numerous applications in social and behavioral sciences, life sciences, economics, computer science, and many more fields.
In this introductory course, two types of graphical models are briefly introduced: undirected (Markov random fields) and directed (Bayesian networks). I will discuss how we can apply these models to estimate the dependencies between variables of interest in the form of a network. We will review excellent real-world examples and exercises (using R) to reinforce learning.
This course will open a new door for you and enable you to see the dependencies from a network point of view, which is much more comprehensive.
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.
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.
Our world has an abundance of so-called complex systems. These are typically large collections of connected elements that influence each other. In this 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.
This course 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.
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.
This is a five-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 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.
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.
We offer a five-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.
This course introduces all the essential ingredients needed to start Bayesian estimation and inference. We discuss specifying priors, obtaining the posterior, prior/posterior predictive checking, sensitivity analyses, and the usefulness of a specific class of priors called shrinkage priors. We propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics). We have prepared many exercises to enable students to get hands-on experience.
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 R and R Studio 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.
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 R statistical software. Knowledge of R is beneficial but not required if you have worked with SPSS, Stata, SAS or other statistical software already. Topics include coverage errors, complex sampling, nonresponse adjustment, measurement error, and analysis of incomplete data. Special attention will be given to the analysis of complex surveys that include weighting, stratification and design effects.
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. 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.
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.
This course in ‘advanced survey design’ takes students beyond the introductory courses and discusses the state of the art in both the design and the analysis of modern survey data, with a focus on new types of data. We present new ways to analyse modern surveys, including non-probability survey designs, smartphone data collection, digital trace data and data collected 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.
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 crash course in data practices and digital methods.