{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "***\n", "***\n", "# 计算新闻传播学\n", "\n", "## 课程简介\n", "***\n", "***\n", "\n", "王成军 \n", "\n", "wangchengjun@nju.edu.cn\n", "\n", "计算传播网 http://computational-communication.com\n" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-05-06T13:30:44.679188Z", "start_time": "2018-05-06T13:30:44.613497Z" }, "slideshow": { "slide_type": "slide" } }, "source": [ "![title](img/webavatar.png)\n", " \n", "\n", "http://computational-communication.com" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-05-06T14:16:44.794569Z", "start_time": "2018-05-06T14:16:44.757499Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "'0.7.2'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mistune\n", "mistune.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-05-06T14:16:47.242153Z", "start_time": "2018-05-06T14:16:47.237267Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "'

\\n'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mistune.markdown('\\n \\n', escape=False)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "https://github.com/jupyter/nbconvert/issues/328\n", "\n", "How do I convert `` tags in markdown cells when exporting in Jupyter? [StackOverflow](https://stackoverflow.com/questions/45245529/how-do-i-convert-img-tags-in-markdown-cells-when-exporting-in-jupyter/50200760#50200760)\n", "\n", "\n", "\n", "\n", "> # conda install mistune=0.7.2" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 内容\n", "- 时间安排\n", "- 课程资料\n", "- 授课计划\n", "- 课前准备" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 时间安排\n", "\n", "- 36学时，两学分\n", "\n", "\n", "\n", "| 时间 | 上午 | 下午 |晚上 | 课时数量 |\n", "| -------------|:-------------:|:-------------:|:-------------:|-----:|\n", "| 2018-04-27 周五 | 9:00-12:00 | 13:30-17:30 | 晚上有课，下午延长半个小时 | 6学时|\n", "| 2018-04-28 周六 | 9:00-12:00 | 15:30-17:30 | 18:30-19:30 授课 19:30-20:30 作业&答疑 | 6学时|\n", "| 2018-04-29 周天 | 9:00-12:00 | 14:00-17:00 | 作业&答疑 | 6学时|\n", "| 2018-05-04 周五 | 9:00-12:00 | 13:30-17:30 | -- | 6学时|\n", "| 2018-05-05 周六 | 9:00-12:00 | 14:00-17:00 | 作业&答疑 | 6学时|\n", "| 2018-05-06 周天 | 9:00-12:00 | 14:00-17:00 | 作业&答疑 | 6学时|\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 课程资料\n", "- (包括数据、PPT、可视化、图片、代码) http://github.com/computational-class/cjc/\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 授课计划\n", "\n", "- 一、[计算新闻传播学简介](01.intro2cjc.ipynb)\n", "- 二、[数据科学的编程工具：大数据(1h)](02.bigdata.ipynb)\n", "- 三、[数据科学的编程工具：Python使用简介（3h）](03.python_intro.ipynb)\n", "---\n", "\n", "- 四、数据抓取：抓取两会报告、[Beautifulsoup](04.PythonCrawler_beautifulsoup.ipynb)\n", "- 五、数据抓取：抓取天涯论坛帖子\n", "- 六、数据清洗：清洗推特数据\n", "- 七、数据清洗：清洗占中新闻、清洗天涯论坛帖子\n", "---\n", "- 八、计算传播与统计初步: 分析天涯论坛的帖子\n", "- 九、计算传播与机器学习: 分析天涯论坛的帖子" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# 授课计划\n", "\n", "- 十、文本挖掘简介\n", "\n", "---\n", "- 十一、基于机器学习的情感分析\n", "- 十二、主题模型\n", "- 十三、计算传播应用：推荐系统简介\n", "---\n", "\n", "- 十四、计算传播应用：推荐系统实践\n", "- 十五、网络科学理论简介\n", "- 十六、网络科学模型\n", "---\n", "\n", "- 十七、网络科学实践：使用networkx分析网络结构\n", "- 十八、课程总结与学生研究项目展示" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 课前准备\n", "\n", "- 下载&安装anaconda python https://www.continuum.io/downloads\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 课前准备\n", "\n", "- 申请graphlab的学术版本，获得序列号即可\n", " - https://dato.com/download/academic.html \n", " - https://turi.com/download/academic.html\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 课前准备\n", "\n", "- Python入门 http://www.imooc.com/view/177\n", "- [Beginning Python 《Python基础教程》](http://book.douban.com/subject/5948760/)\n", "- 用Python玩转数据 http://www.icourse163.org/course/nju-1001571005#/info" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 课前准备\n", "- 注册github账户 http://github.com\n", "- watch本课程repo: https://github.com/computational-class/cjc/\n", "- 下载并安装github desktop客户端 （建议：非必须内容）： https://desktop.github.com/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Python's Role in Big Data Analytics\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# This is the End.\n", "> Thank you for your attention!" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "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": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "100px", "left": "1233.77px", "top": "112.979px", "width": "411px" }, "toc_section_display": true, "toc_window_display": true }, "toc_position": { "height": "158px", "left": "1166.02px", "right": "20px", "top": "120px", "width": "312px" } }, "nbformat": 4, "nbformat_minor": 1 }