Our upcoming courses at Helmholtz Munich
Date: 28 September 2023
Time: 9:00 - 13:00
Format: online
Registration: email us
“Reproducible and Open Research” provides a broad overview on different aspects of open research and reproducibility. This includes the fields of technical, statistical and computational reproducibility. With a focus on the latter two aspects. The course consists of lessons on different aspects of open and reproducible research and offers the opportunity to discuss about experiences and expectations on this topic. Various other features of Open Science will also be discussed. Furthermore, some hand-on examples for how to implement these methods in your daily work are provided.
Prerequisites: None. However, basic knowledge in statistics and programming is advantageous.
Date: 04, 05 October 2023
Time: 9:00 - 17:00
Format: online
Registration: Email to Anna Polla
“Graphics with R” covers methods in customizing classical R graphics. For example, manipulating axes, combining multiple figures in one graphic and different color schemes. Additionally, an introduction to ggplot2, an advanced and powerful tool for building graphics with R, is provided. In this course you will learn how to create refined, meaningful graphs in R to visually describe your research outputs.
Prerequisites: Programming skills with R, e.g. course “Introduction to R”
Date: 10, 11, 17 October 2023
Time: 9:00 - 17:00
Format: online
Registration: Email to Anna Polla (fully booked)
In the “Multivariate Statistics 1 - Principal component analysis and clustering algorithms” course you will learn when and how to apply unsupervised learning methods such as PCA for dimension reduction and k-means, hierarchical clustering or some hybrid approaches for clustering. The course also covers some rotation techniques after dimensionality reduction as well as well as mixture models and heatmaps (clustering). The course will help to understand the basis of the theory when doing a multivariate analysis. All topics are accompanied with hands-on exercises using the statistical software R. The participants are invited to ask as many questions as they want about the analyses on their own dataset.
Prerequisites: Programming skills with R, e.g. course “Introduction to R” and basic knowledge of statistics, e.g. course “Introduction to Statistics”. Some practice in ggplot2 is also welcome (e.g. course “Graphics with R”).
Date: 18 October 2023
Time: 9:00 - 17:00
Format: hybrid (Campus Neuherberg and via Zoom)
Registration: Email to Federico Tamayo
"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: 24 October 2023
Time: 9:00 - 17:00
Format: online
Registration: Email to Federico Tamayo
“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: 07, 08, 16, 17 November 2023
Time: 13:30 - 17:00
Format: online
Registration: Email to Anna Polla
“Mixed Models” address datasets containing multiple measurements of the same individuals or of groups, a situation in which classical statistical approaches are biased. In this course, we begin with a summary of linear models and their limitations, then explain “Mixed Models”, their applicability, and usage. We will cover random intercept and random slope models in detail. Besides discussions on the interpretation and theory also ideas how to run the models with R and exercises will be provided.
Prerequisites: Programming skills with R, e.g. course “Introduction to R” and knowledge of regression models, e.g. course “Introduction to Statistics”. Some practice in ggplot2 is also welcome.
Date: 09, 10 November 2023
Time: 09:00 - 17:00
Format: offline (Campus Neuherberg)
Registration: Email to Anna Polla
In the “Multivariate Statistics 2” course you will learn when and how to unsupervised and supervised dimension reduction techniques as MDS, MFA, t-SNE, UMAP, PCR or PLSR. A short introduction on PCA will be given at the beginning of the lecture (more details on PCA can be learned in the course Multivariate Statistics 1). The course will help to understand the basis of the theory when doing a multivariate analysis. All topics are accompanied with hands-on exercises using the statistical software R. The participants are invited to ask as many questions as they want about the analyses on their own dataset.
Prerequisites: Programming skills with R, e.g. course “Introduction to R”, basic knowledge of statistics, e.g. course “Introduction to Statistics” and knowledge on basic dimension reduction techniques (PCA), e.g. course “Multivariate Statistics 1”. Some practice in ggplot2 is also welcome (e.g. course “Graphics with R”).
Date: 14, 15, 21 November 2023
Time: 09:00 - 17:00
Format: online
Registration: Email to Anna Polla
“Introduction to Python” provides a foundation for coding with the programming language Python. The course is designed for applied researchers with no or low previous programming skills in Python. In this course you will learn the basics of coding in Python. In particular, you will learn how to read and manipulate datasets, do basic calculations and plots. These skills are necessary for performing any subsequent course applying Python like Scanpy.
Prerequisites: Basic knowledge in Programming and Statistics is recommended, but not mandatory, e.g. courses "Introduction to R" or "Introduction to Statistics"
These courses are designed for employees of Helmholtz Munich. For registration please contact the organizer via email (see items above). For general questions please contact us via email.
For employees of other Helmholtz centers we offer similar courses at HIDA.
Our plan of all courses in 2023 at Helmholtz Munich is described in this document. These courses will open for registration staggered through out the year.