Skip to content
Material for the EPFL master course "A Network Tour of Data Science", edition 2019.
Jupyter Notebook TeX Makefile Python
Branch: master
Clone or download
mdeff Merge pull request #4 from mdeff/projects
dataset descriptions for projects
Latest commit bf4a5bb Oct 14, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
assignments rename notebook Oct 2, 2019
projects some updates Oct 14, 2019
slides add scikit-learn Sep 18, 2019
tutorials fix headers Oct 7, 2019
.gitignore gitignore Sep 17, 2019
LICENSE.txt license Sep 17, 2019
README.md Merge pull request #4 from mdeff/projects Oct 14, 2019
environment.yml dgl instead of pytorch-geometric (easier to install) Sep 23, 2019
makefile
test_install.ipynb dgl instead of pytorch-geometric (easier to install) Sep 23, 2019

README.md

A Network Tour of Data Science, edition 2019

Binder

This repository contains the material for the practical work associated with the EPFL master course EE-558 A Network Tour of Data Science (moodle), taught in fall 2019. The content is similar to the 2017 and 2018 editions, with more emphasis on machine learning with graphs. Compared to the 2016 edition, the course has been refocused on graph and network sciences. The course is divided in two parts: Network Science and Learning with Graphs. The material revolves around the following topics:

  1. Network Science,
  2. Spectral Graph Theory,
  3. Graph Signal Processing,
  4. Data Science,
  5. Machine Learning.

Theoretical knowledge is taught during lectures. Practical knowledge is taught through tutorials. Both are practiced and evaluated through two assignments and a semester project. Below are slides about the organization of the course.

  1. Course organization
  2. Project expectations
  3. Concluding remarks

Tutorials

Below is the teaching material you'll find in this repository (tentative).

  1. Installation instructions
  2. Introduction
  3. Building graphs from edge lists
  4. Building graphs from features
  5. Manipulating graphs with NetworkX
  6. Plotting with matplotlib
  7. Interactive graph visualization with gephi
  8. Sparse matrices with scipy
  9. Linear algebra for graphs and networkx
  10. Graph signal processing with pygsp
  11. Learning on graphs with pytorch

For this course, you'll use the following tools: conda & anaconda, python, jupyter, git, numpy, scipy, matplotlib, pandas, networkx, graph-tool, pygsp, gephi, scikit-learn, pytorch.

Assignments

The following assignments were designed to evaluate the theoretical understanding of students through practice. As a Data Science course, those activities are realized on real data and networks.

  1. Network science: assignment, solution, feedback.
  2. Learning with graphs: assignment, solution, feedback.

Projects

Part of the course is evaluated by an open-ended project (see the description), proposed and carried out by groups of four students. We provide a list of datasets and project ideas. Those data projects are meant to jointly practice and evaluate their theoretical network analysis skills and practical Data Science skills. Below is the work of the 150 students enrolled that year.

  • [report, slides, code] Project Title

As each team stored their code in a github repository, all their code can conveniently be downloaded with git clone --recurse-submodules https://github.com/mdeff/ntds_2019. One folder per team will be populated in projects/code.

Installation

Click the binder badge to play with the notebooks from your browser without installing anything.

Another option is to use the EPFL's JupyterHub service, available at https://noto.epfl.ch. While the default environment has most packages pre-installed, you can create different environments (e.g., for different classes). To do so, follow the instructions contained in the notebooks supplied in the Documentation folder that is available on your Noto instance.

For a local installation, you will need git, Python, and packages from the Python scientific stack. If you don't know how to install those on your platform, we recommend to install Miniconda or Anaconda, a distribution of the conda package and environment manager. Follow the below instructions to install it and create an environment for the course.

  1. Download the Python 3.x installer for Windows, macOS, or Linux from https://conda.io/miniconda.html and install with default settings. Skip this step if you have conda already installed (from Miniconda or Anaconda).
    • Windows: double-click on Miniconda3-latest-Windows-x86_64.exe.
    • macOS: double-click on Miniconda3-latest-MacOSX-x86_64.pkg or run bash Miniconda3-latest-MacOSX-x86_64.sh in a terminal.
    • Linux: run bash Miniconda3-latest-Linux-x86_64.sh in a terminal or use your package manager.
  2. Open a terminal. Windows: open the Anaconda Prompt from the Start menu.
  3. Install git with conda install git.
  4. Navigate to the folder where you want to store the course material with cd path/to/folder.
  5. Download this repository with git clone https://github.com/mdeff/ntds_2019.
  6. Enter the repository with cd ntds_2019.
  7. Create an environment with the packages required for the course with conda env create -f environment.yml.
  8. Run the steps below to start Jupyter. You should be able to run the test_install.ipynb notebook.

Every time you want to work, do the following:

  1. Open a terminal. Windows: open the Anaconda Prompt from the Start menu.
  2. Activate the environment with conda activate ntds_2019.
  3. Navigate to the folder where you stored the course material with cd path/to/folder/ntds_2019.
  4. Start Jupyter with jupyter lab. The command should open a new tab in your web browser.
  5. Edit and run the notebooks from your browser.
  6. Once done, you can run conda deactivate to leave the ntds_2019 environment.

Team

License

The content is released under the terms of the MIT License.

You can’t perform that action at this time.