Course by tag

This online course covers how time series models can be used to model the dynamics of intensive longitudinal data (ILD). We focus on ILD from the behavioural sciences, typically collected using questionnaires with ambulatory assessments (AA), the experience sampling method (ESM), ecological momentary assessments (EMA), or daily diaries. We explain the basics of simple and more advanced modelling approaches, the philosophies behind them, and caveats to consider.

If you are looking for a powerful programming language, you should learn Python, a language with a simple syntax and a powerful set of libraries. 
It is easy for beginners to learn Python and it is widely used in many scientific fields for data exploration. 
This online workshop is an introduction to the Python programming language and, in particular, is geared toward people who are new to the language and who have relatively little experience with other programming languages.
So, for beginners and people with little experience in programming, this course would be an excellent choice.
In this online Python training workshop, you learn to program in Python 3.

This online 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.

Unlock the Power of Network Inference Methods in the Real World, All in One Exciting Day!
In this introductory course, I will provide a brief overview of the two types of graphical models: undirected (Markov random fields) and directed (Bayesian networks). I will explain how these models can be applied in the real world and how to estimate (and interpret) the dependencies between variables of interest (in the form of a network) using various inference methods. We will also explore practical examples (using real data) and exercises (using R) to reinforce your learning.

Linear regression is one of the most ubiquitous statistical methods. Most statistical techniques can be viewed as either special cases of linear regression (e.g., t-tests, ANOVA) or generalizations of linear regression (e.g., multilevel modeling, SEM, neural networks, GLM, survival analysis). In this workshop, participants will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor variables, moderation, and diagnostics. Workshop participants will practice what they learn via practical exercises.

Missing data are ubiquitous in nearly every data analytic enterprise. Simple ad-hoc techniques for dealing with missing values such as deleting incomplete cases or replacing missing values with the item mean can cause a host of (hidden) problems. In this workshop, we will discuss principled methods for treating missing data and how to apply these methods in R. We will cover some basic missing data theory and two principled methods for treating missing data: multiple imputation (MI) and full information maximum likelihood (FIML). Participants will practice what they learn via practical exercises.

This workshop will introduce participants to the R statistical programming language. R is a completely free and open-source programming language and environment for statistical analysis. In this workshop, participants will learn what R is and how it differs from other statistical software packages and programming languages. They will learn the basics of data I/O, manipulation, and visualization in R. Workshop participants will practice what they learn via practical exercises.

In this one-day course, participants will be introduced to informative hypotheses, which are alternatives for the traditional null and alternative hypotheses, and to the AIC-type criterion GORIC and the Bayes factor, which are alternatives for the p-value. This course is applied, hands-on, and build around concepts and not formulas.