Course by tag

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 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.

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.

If you expect to work with the software Mplus, this course can help you to get started!  This course is a compact one-day workshop introducing Mplus. We will focus on preparing data for Mplus, introducing common model syntax, avoiding common mistakes, interpreting output, and dealing with common error messages. Practice exercises demonstrate multiple regression and factor analysis models, which are the basis of structural equation models in Mplus.

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, methods for exploring/quantifying the extent of the missing data problem and two principled methods for correcting the missing data: multiple imputation and full information maximum likelihood. Participants will practice what they learn via practical exercises.

This workshop introduces 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. We will also cover basic statistical analyses such as t-tests, correlation, ANOVA, and linear regression. Workshop participants will practice what they learn via practical exercises.

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