We are excited to announce our upcoming course on Introduction to the Analysis of Longitudinal Data with R! This course is designed to provide you with comprehensive knowledge and practical skills to effectively analyze and interpret longitudinal data using the R programming language.
Longitudinal data, which involve repeated measurements over time or space, pose unique challenges in analysis and interpretation. In this course, we will explore the main challenges associated with longitudinal data from both classical statistical and machine learning perspectives. Specific topics covered will include forecasting, epidemiology, and gene-expression experiments. You will gain insights into visualization, exploratory data analysis, modeling, and validation techniques for longitudinal data analysis.
The course is structured into modules spanning four days of intensive learning. Each day will feature engaging lectures accompanied by class discussions on key concepts. Practical hands-on sessions will be conducted, enabling you to apply the acquired skills through collaborative exercises. These exercises will encourage interaction with instructors and fellow students, fostering a dynamic learning environment. Results will be interpreted and discussed throughout the exercises. Towards the end of the course, we will conduct a Kahoot quiz to recap and highlight the essential concepts covered. Additionally, ample time will be provided for discussing specific research problems and participant questions.
TARGET AUDIENCE AND ASSUMED BACKGROUND
This course is designed for advanced students, researchers, and professionals interested in analyzing longitudinal data in real-life applications within the field of biology. Whether you are an absolute beginner or an experienced user seeking to enhance your understanding of longitudinal models and scripting code, this course is suitable for you. We will start with an introduction to general concepts and approaches for dealing with longitudinal data. Subsequently, we will explore applications in forecasting, epidemiology, and gene expression. While a background in biology and familiarity with inferential and predictive experiments is beneficial, attendees from various disciplines are welcome. The course will primarily utilize R/Python, Markdown/Jupyter Notebooks, and the Linux command line. Although a basic understanding of R programming and the Linux environment is advantageous, it is not mandatory.
By the end of the course, you will have gained:
- The ability to recognize and address spatial and temporal dependencies in your data.
- Proficiency in the most common methods for analyzing data with repeated records.
- Knowledge and principles of data forecasting.
- Insight into specific applications of longitudinal data analysis in domains such as epidemiology and gene expression experiments.
- The skills to design, analyze, and interpret scientific experiments with a time component.
Don't miss this opportunity to enhance your expertise in the analysis of longitudinal data with R! Join us for an enriching learning experience that combines theoretical foundations with hands-on practical exercises.