mybinder.org (and other BinderHubs) are frequently used for teaching. This thread is a place to collect courses and material that exists. The main purpose is to advertise these courses, share knowledge and give examples to those who are thinking of using a BinderHub for a course and are looking for inspiration and examples.
This post itself is a “wiki” post, this means you can directly edit it to add a course you know of. Please try and follow the format used by other courses (or update all of them to keep things consistent ♂.
You won’t be able to edit this post until you have spent some time in this community and contributed in other threads. One way to get started is to head over to the “Introduce yourself” thread.
The content and methods taught are intended for a target audience of scientists and engineers who need to use computational methods and data processing in their work, but typically have no prior programming experience or formal computer science training.
This OER is designed to introduce users of experimental techniques, such as small angle scattering and diffraction to classical simulation methods. More and more the analysis of these experimental methods is leveraging classical simulation, however, the experimentalists have rarely received formal training in simulation methods. This course aims to fill that gap. Built on jupyter-books with interactivity via either thebelab or a MyBinder resource. Recently submitted a publication introducing the resource, a preprint of which can be found on arXiv:1902.01324.
Taught by Alfredo Canziani the target audience is for students or scientists and engineers that have little experience with machine learning or PyTorch. The whole course has also been recorded and the playlist is made available here.
A lecture course (in German) at the LMU in Munich on Waves and Optics.
In this class, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Through a strong emphasizes on data centric computing, quantitative critical thinking, and exploratory data analysis this class covers key principles and techniques of data science. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
Curso de métodos computacionales avanzados en la Universidad de los Andes (Colombia).
This textbook teaches how to test software, in particular how to generate tests automatically. It comes in 25+ chapters, all written as Jupyter notebooks, and all directly editable in MyBinder. Comes with significant infrastructure to derive various formats (PDF, slides, code, Python packages, etc.).
This is a short introduction to Jupyter notebooks and the Python programming language for data analysis. This course was designed as a first introduction to programming for first year chemistry students at the University of Bath. However, the skills introduced are relatively general for numerical data analysis and plotting.
Master course taught at EPFL. The course material revolves around the following topics: Network Science, Spectral Graph Theory, Graph Signal Processing, Data Science, Machine Learning. Theoretical knowledge is taught during lectures. Practical knowledge is taught through tutorials. Both are practiced and evaluated through a semester project.
Presented at the GraphSiP summer school. The material covers the following topics:
- Graphs: creation, models, properties, visualization
- Spectral Graph Theory: spectral clustering, Laplacian eigenmaps
- Graph signals: gradient, divergence, smoothness
- Fourier: modes, transform
- Filters: filterbanks, filtering, approximations
- Applications to point clouds: denoising and curvature estimation
- Applications to neuroscience: fMRI signals on brain connectome
Participants will learn how to identify network data, how to deal with it, and what can be learned from it. They will know the basics of information processing over networks, and how to devise a machine learning system based on network data. Finally, the hands-on experience will give them the confidence to apply those tools in practice, in applications of their choice.