Our upcoming courses at Helmholtz Munich
Date: 12, 13 February 2025
Time: 9:00 - 12:30
Format: online
Registration: CaMS
“Graphics with R” covers methods in customizing R graphics using ggplot2. ggplot2 is an advanced and powerful tool for building graphics with R. In this course you will learn how to create refined, meaningful graphs in R to visually describe your research outputs. For example, we will learn how to manipulate axes, combine multiple figures in one graphic and different color schemes.
Prerequisites: Programming skills with R, e.g. course “Introduction to R”
Before 2025 we also provided an introduction to base R graphics. This is not part of the course anymore.
Date: 12, 13 February 2025
Time: 13:30 - 17:00
Format: online
Registration: CaMS
“Version control using Git and RStudio” provides a foundation for applying version control in your daily work. Git is a modern version control environment. Classical applications involve Gitlab and GitHub. The course is designed for applied researchers with no or low previous knowledge in using Git. However, we recommend to have programming skills, for example in R, Python, or similar. In this course you will learn the basics of how to get started with Git and will achieve a basic understanding how Git works. Further, you will learn how to run version control using remote servers like Gitlab. During the course we will discuss graphical user interface options to apply version control (RStudio) and show options for command line usage.
Prerequisites: Basic programming skills, e.g. experience with R or Python.
Date: 18, 19, 25, 26 February 2025
Time: 09:00 - 12:30
Format: online
Registration: CaMS
In the Introduction to Machine Learning course, we delve into the practical application of fundamental machine learning techniques for data analysis using Python. This course is designed for individuals who want to start using machine learning for data analysis, focusing less on traditional statistics and more on predictive modeling to classify data or predict outcomes. The course is taught interactively with live coding using Jupyter Notebook.
By the end of the course, you will be able to confidently select and utilize basic machine learning techniques, effectively interpret your findings, and apply them to real-world scenarios.
Prerequisites: Programming skills with Python, e.g. course Introduction to Python. Basic understanding of statistical methods, in particular regression analysis, is recommended, e.g. course Introduction to Statistics.
Date: 18, 19, 25, 26 February 2025
Time: 13:30 - 17:00
Format: online
Registration: CaMS
“Survival Analysis” deals with statistical models for analyzing time-to-event data. These are applicable to classical survival analysis (time until death) or other time-to-event settings (recurrence of a tumor, discharge from a hospital or time until a machine breaks). The methods we will discuss include the Cox proportional hazards model and parametric regression models such as accelerated failure time models.
Prerequisites: Programming skills with R, e.g. course “Introduction to R” and basic knowledge of regression models and hypothesis testing, e.g. course “Introduction to Statistics”. Basic knowledge on applying ggplot2 functions is advantageous but not mandatory (e.g. course “Graphics with R”).
Date: 11, 12, 18, 19 March 2025
Time: 9:00 - 12:30
Format: online
Registration: CaMS
In the Advanced Methods in Machine Learning course, we go beyond the most basic approaches used in Machine Learning for classification and regression. We will explore Support Vector Machines, ensemble methods like Random Forests and Boosting, and introduce the fundamentals of Deep Learning using convolutional networks. Furthermore, we also cover sampling techniques for robust model evaluation, measuring estimation confidence, and handling imbalanced datasets. By the end, you will have an overview of some of the most important techniques in Machine Learning, can apply these methods in real-world scenarios, and have a basic understanding how Deep Learning can be applied using PyTorch.
Prerequisites: Python programming skill, as taught in the Introduction to Python course and basic knowledge of Machine Learning and model evaluation, e.g. Introduction to Machine Learning course.
Date: 11, 12 March 2025
Time: 13:30 - 17:00
Format: online
Registration: CaMS
"RMarkdown" is a fantastic tool to write efficiently manuscripts and research reports. It relieves the work to get your manuscripts and reports reproducible and transferable. You combine the coding in R and writing of the documentation/interpretation of the results all in the same file and compile it directly as a report (e.g. as html, pdf or word document). No copy pasting from the statistical analysis tool to the documentation file anymore. After this course, you will no longer need to create multiple files in order to produce one single research document. Tables, figures, citations and much more can be included in one single document to be directly shared with your peers or PIs.
Prerequisites: Programming skills with R, e.g. course “Introduction to R”
Date: 8, 9 April 2025
Time: 9:00 - 17:00
Format: Campus Neuherberg
Registration: CaMS
“Introduction to R” provides a foundation for coding with the statistical programming language R. The course is designed for applied researchers with no previous programming skills. In this course you will learn the basics of coding in R. In particular, you will learn how to read and manipulate datasets. These skills are necessary for performing any subsequent statistical analyses in R.
Prerequisites: None
The plan for 2025 is available HERE. Registration opens some weeks prior to the course, please check CaMS for details. Please consider that dates might change slightly throughout the year.
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These courses are designed for employees of Helmholtz Munich. Please use CaMS for registration. For questions please contact us via email.
For employees of other Helmholtz centers we offer similar courses at HIDA.