code/0.about2cjc.ipynb

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    "\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"
   ]
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   "source": [
    "![title](img/webavatar.png)\n",
    " \n",
    "\n",
    "http://computational-communication.com"
   ]
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     "data": {
      "text/plain": [
       "'0.7.2'"
      ]
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     "execution_count": 1,
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   "source": [
    "import mistune\n",
    "mistune.__version__"
   ]
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       "'<p><img src=\"link\" align=\"right\" width=100></p>\\n'"
      ]
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   "source": [
    "mistune.markdown('\\n <img src=\"link\" align=\"right\" width=100>  \\n', escape=False)"
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   "source": [
    "https://github.com/jupyter/nbconvert/issues/328\n",
    "\n",
    "How do I convert `<img>` 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"
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  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "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": {
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   },
   "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": {
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    }
   },
   "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": {
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   },
   "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"
   ]
  },
  {
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   "metadata": {
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   "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": {
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   },
   "source": [
    "# 课前准备\n",
    "\n",
    "- 下载&安装anaconda python https://www.continuum.io/downloads\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# 课前准备\n",
    "\n",
    "- 申请graphlab的学术版本，获得序列号即可\n",
    "    - <del>https://dato.com/download/academic.html \n",
    "    - https://turi.com/download/academic.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "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": {
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   },
   "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": {
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    }
   },
   "source": [
    "# Python's Role in Big Data Analytics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# This is the End.\n",
    "> Thank you for your attention!"
   ]
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