11/01/2024
The demand for skilled data analysts is soaring higher than ever. Elevate your career with our Data Analysis Training Programmes for 2024:
More info on our Beta-Academy website: https://beta-academy.ugent.be/
• Module 9 - Getting Started with NVivo for Qualitative Data
NVivo is a widely used computer assisted qualitative data analysis software package which provides a potentially useful tool for the management and analysis of qualitative research data. This course is intended as a basic introduction to using NVivo for qualitative data analysis.
• Module 10 - High Dimensional Data Analysis
Modern high throughput technologies easily generate data on thousands of variables; e.g. health care data, genomics, chemometrics, environmental monitoring, web logs, movie ratings, … Conventional statistical methods are no longer suited for effectively analysing such high-dimensional data. Modern advances in statistical data analyses allow for the appropriate analysis of such data.
• Module 11 - Explaining and Predicting Outcomes with Linear Regression
Linear regression addresses how a continuous dependent variable is associated by one or more predictors of any type. The fact that many practical problems deal with continuous outcomes (e.g. income, blood pressure, temperature, affect) makes linear regression a popular tool.
• Module 12 - Machine Learning with Python
Many modern digital applications increasingly rely on machine learning as a means to derive predictive strength from high-dimensional data sets. Compared to traditional statistics, the absence of a focus on scientific hypotheses, and the need for easily leveraging detailed signals in the data require a different set of models, tools, and analytical reflexes.
• Module 13 - Structural equation modeling with lavaan
Structural equation modeling (SEM) is a general statistical modeling technique to study the relationships among observed and latent variables. It spans a wide range of multivariate methods including path analysis, mediation analysis, confirmatory factor analysis, growth curve modeling, and many more. Many applications of SEM can be found in the social, economic, behavioral and health sciences, but the technology is increasingly used in disciplines like biology, neuroscience and operation research.
