{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "![](images/EscUpmPolit_p.gif \"UPM\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Course Notes for Learning Intelligent Systems" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction to Network Analysis\n", " \n", "In this session, we are going to get more insight regarding how to analyze and visualize social networks.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Objectives\n", "\n", "The main objectives of this session are:\n", "* Understanding why networks are important in data science\n", "* Experimenting with network analysis with networkx." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Table of Contents" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "1. [Home](0_Intro_Network_Analysis.ipynb)\n", "2. [First Steps](1_First_Steps.ipynb)\n", "3. [Working_with_Graphs](2_Working_with_Graphs.ipynb)\n", "4. [Network Analysis](3_Network_Analysis.ipynb)\n", "5. [Social Networks](4_Social_Networks.ipynb)\n", "6. [Pandas integration](5_Pandas.ipynb)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Licence\n", "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", "\n", "© Carlos A. Iglesias, Universidad Politécnica de Madrid." ] } ], "metadata": { "celltoolbar": "Slideshow", "datacleaner": { "position": { "top": "50px" }, "python": { "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" }, "window_display": false }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }