**Python programming and Network models**

ff2n9b47 -- Tue 10-12, North (Észak) 3.92 -- Feb 9,16,25, Mar 1,3(8-10am),8,22, Apr 12,19,26, May 3,10

libraries: http://graph-tool.skewed.de, http://igraph.org/python

**2016-04-19:** Random graph and small world model

**2016-04-12:** Network properties → Starting **Student projects**

**2016-03-22:** Mapping a small part of the WWW → **Homework & Solutions**

**2016-03-08:** Pattern matching → **Homework & Solutions**

**2016-03-03:** Generating Scale-Free (SF) network, computing its degree distribution

**2016-03-01:** Giant component of ER network → **Homework & Solutions**

**2016-02-23:** Generating ER network faster, speed tests

**2016-02-16:** Generating random (Erdos-Renyi) network → **Homework & Solutions**

**2016-02-09:** Mean and std.dev. of N rnd numbers → **Homework & Solutions**

**2016-02-01:** Please install Python and a text editor for coding on your laptop. Click here for help.

This course is an updated version of a previous course on Perl programming and networks.
It aims to help students reach the level where they can routinely apply and combine two major tool sets of
current quantitative research: Python and Network models.
Currently, **Python** is a major programming language
(i) in physics from the microscopic to the largest length scales,
(ii) in computational biology,
(iii) in large-scale social and technological networks, and other fields.
**Networks** provide a quantitative tool set for analyzing many-particle interacting systems and complex data.
They are intuitive and can be efficiently connected to linear algebraic, stochastic and other methods.
Python is taught through examples from "Learning Python" and "Learn Python the Hard Way", while networks are taught with the "Network Science Book". Homework is largely based on examples taken from the previous version of this course.

Fall 2015:
with Gergely Palla the course of Tamas Vicsek on the Statistical physics of biological systems.

Before 2015:
Perl programming and Networks (several versions).