A list of courses that use Binder

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 :bowing_man:‍♂.

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.


Introduction to Python for Computational Science and Engineering

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.


The interaction between simulation and scattering

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.


Mini Course in Deep Learning with PyTorch

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.


Elektromagnetische Wellen und Optik

A lecture course (in German) at the LMU in Munich on Waves and Optics.


Principles and Techniques of Data Science

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.


Métodos Computacionales Avanzados

Curso de métodos computacionales avanzados en la Universidad de los Andes (Colombia).


Generating Software Tests

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.).


Data Analysis with Jupyter Notebooks

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.


A Network Tour of Data Science

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.


Graph Signal Processing tutorial

Presented at the GraphSiP summer school. The material covers the following topics:

  1. Graphs: creation, models, properties, visualization
  2. Spectral Graph Theory: spectral clustering, Laplacian eigenmaps
  3. Graph signals: gradient, divergence, smoothness
  4. Fourier: modes, transform
  5. Filters: filterbanks, filtering, approximations
  6. Applications to point clouds: denoising and curvature estimation
  7. Applications to neuroscience: fMRI signals on brain connectome

Learning and Processing over Networks workshop

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.

Nuclear Power Economics and Fuel Management

(NPRE 412 at the University of Illinois at Urbana-Champaign taught by Professor Katy Huff)

Quantitative analysis of the impact of the nuclear power industry; nuclear fuel cycle and capital costs for thermal and fast reactors; optimization of the use of nuclear fuels to provide the lowest energy costs and highest system performance; comparison between fossil fuel systems, fission systems, and controlled thermonuclear fusion systems.

1 Like

Could you leave instructions on how to edit this “Wiki” post? I found plenty of functionality, but seem to be missing the one “Edit” button.

@zeller I also encountered this issue, I believe that you cannot edit the ‘wiki’ post until you have been promoted a trust level. This comes from spending some time on the webpage.

Ok. If someone with the necessary privileges could simply add a link, then – thanks!

Generating Software Tests

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.)

2 Likes

I didn’t realise that the edit button would be hidden from new members. I updated the top post to mention this and pointed people at the “Introduce yourself” thread.

1 Like

Here are mines. I’d be glad if someone with edit permissions could update the wiki.

A Network Tour of Data Science
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.

Graph Signal Processing tutorial
Presented at the GraphSiP summer school. The material covers the following topics:

  1. Graphs: creation, models, properties, visualization
  2. Spectral Graph Theory: spectral clustering, Laplacian eigenmaps
  3. Graph signals: gradient, divergence, smoothness
  4. Fourier: modes, transform
  5. Filters: filterbanks, filtering, approximations
  6. Applications to point clouds: denoising and curvature estimation
  7. Applications to neuroscience: fMRI signals on brain connectome

[Learning and Processing over Networks workshop] (github/rodrigo-pena/amld2019-graph-workshop, I’m only allowed to put two links…)
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.

2 Likes

Thanks for the additions! I updated the top post. If you contribute a bit more (by introducing yourself) you should soon gain the right to edit wiki posts :).

1 Like

Thanks! The last link misses the .com (“github” => “github.com”)

1 Like