Course by tag

Collaborative Data Science

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.

Data Science: 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 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.

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.

Introduction to Graphical Models (for Network Inference) in R: Markov random Fields and Bayesian Networks

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, I briefly introduce two types of graphical models: 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.

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 programs with Python. It starts at the very basics and is explicitly intended for students who have no or only little programming experience.

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.

Modeling the Dynamics of Intensive Longitudinal Data

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).

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 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 Estimation

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 get hands-on experience.

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 R and R Studio 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 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.

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. 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 modern survey data, with a focus on new types of data. We discuss 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.

Exploring Culture through Data: Digital methods & Data practices

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.