If you are looking for a powerful programing 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 widely use it in many scientific areas for data exploration. This workshop is an introduction to the Python programming language and in particular is geared towards people new to the language and who may, or may not, have experience with other programming languages. In this Python training workshop you learn to program in Python 3.
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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.
If you expect to work with the software Mplus, this course can help you to get started! This online course is a compact 1-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 structural equation models in Mplus.
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 generalisations of linear regression (e.g., multilevel modelling, SEM, neural networks, GLM, survival analysis). In this course, students will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor variables, moderation, prediction, and diagnostics. Students 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 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. Students will practice what they learn via practical exercises.
This workshop will introduce students to the R statistical programming language. R is a completely free and open-source programming language and environment for statistical analysis. In this course, students 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 visualisation in R. We will also cover basic statistical analyses such as t-tests, correlation, ANOVA, and linear regression. Students will practice what they learn via practical exercises.
Introduction to JASP and (Bayesian) Hypotheses Evaluation - Free for employees of the Faculty Social Sciences of Utrecht University (FSS-UU)
Since 2018 the first year bachelorstudents of the Faculty of Social and Behavioral Sciences of Utrecht University learn to work with JASP and how to (Bayesianly) evaluate hypotheses using JASP. The course is tailored to lecturers and researchers who want to learn what the bachelor students of FSBS learn about JASP and (Bayesian) hypotheses evaluation (and a little bit more). This course is applied, hands-on, and build around concepts and not formulas.