A Network Tour of Data Science, edition 2017

This repository contains the material for the labs associated with the EPFL master course EE-558 A Network Tour of Data Science (moodle), taught in autumn 2017. Compared to the 2016 edition, the course has been refocused on graph and network sciences. The course material revolves around the following topics:

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

Below is the material you'll find in that repository:

1. Practical informations
2. Installation instructions
3. Introduction: conda & Anaconda, Python, Jupyter, git, scientific Python
4. Network properties: twitter demo, numpy & matplotlib demo, assignment, solution
5. Network models: demo, assignment, solution
6. Spectral graph theory: demo, assignment, solution
7. Graph signal processing: demo, assignment, solution
8. Machine learning: demo, assignment, solution

As a Data Science course, the above activities are realized on real networks, e.g. a social network from Twitter, that students have to collect and clean themselves.

Installation

For these labs we'll 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 Anaconda, a distribution of the conda package and environment manager. Please follow the below instructions to install it.

1. Open https://www.anaconda.com/download and download the Python 3.x installer for Windows, macOS, or Linux.
2. Install with default options.
3. Open the Anaconda Prompt (e.g. from the Windows Start menu).
4. Execute conda config --add channels conda-forge to add the conda-forge channel. It provides many additional packages to conda.
5. Install git with conda install git.
6. Download this repository by executing git clone https://github.com/mdeff/ntds_2017.
7. Open the Anaconda Navigator (e.g. from the Windows Start menu).
8. From there you can start e.g. Jupyter or Spyder.