Regarding to the methodology for the course, the approach followed is practical, focused on the analysis of real data and the deployment (in a virtual environment) of the proposed solutions. We intend knowledge to emerge from the access, processing and analysis of real data, and from the experience in the configuration of network scenarios. The objective is to empower the student to access by himself to the data/experience and build from this input its own knowledge.
Data analysis is a skill growingly required in the job market, that is just assumed to be known by any engineer. In this course we provide basic knowledge to data processing through a programming interface. For real data analysis, we use Python, and in particular the pandas library, a tool providing flexible data processing with a low entry barrier (we devote some course time to present these tools). We apply the tool to real data to analyse in the laboratory how many different networks are in the Internet, which is the distance between them, how many addresses have been assigned to date, who is the owner, which are the top buyers and sellers, how many routers are traversed when accessing to most popular destinations, etc.
On the other hand, we use virtual network topologies (using the CORE virtual network framework) to understand how NATs are configured.