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cjc-gh-pages/ (0, 2021-05-13)
cjc-gh-pages/.DS_Store (14340, 2021-05-13)
cjc-gh-pages/LICENSE (1082, 2021-05-13)
cjc-gh-pages/code/ (0, 2021-05-13)
cjc-gh-pages/code/ mobike_soda.ipynb (681793, 2021-05-13)
cjc-gh-pages/code/.DS_Store (14340, 2021-05-13)
cjc-gh-pages/code/0.about2cjc.ipynb (8852, 2021-05-13)
cjc-gh-pages/code/0.common_questions.ipynb (44002, 2021-05-13)
cjc-gh-pages/code/01.intro2cjc.ipynb (26603, 2021-05-13)
cjc-gh-pages/code/01.jupyter_notebook.ipynb (33133, 2021-05-13)
cjc-gh-pages/code/01.slides.ipynb (24074, 2021-05-13)
cjc-gh-pages/code/02.bigdata.ipynb (12703, 2021-05-13)
cjc-gh-pages/code/03.graphlab.ipynb (16316, 2021-05-13)
cjc-gh-pages/code/03.python_intro.ipynb (913668, 2021-05-13)
cjc-gh-pages/code/03.rpy2.ipynb (294179, 2021-05-13)
cjc-gh-pages/code/03.turicreate.ipynb (4938, 2021-05-13)
cjc-gh-pages/code/04.PythonCrawlerGovernmentReport.ipynb (27819, 2021-05-13)
cjc-gh-pages/code/04.PythonCrawler_beautifulsoup.ipynb (607365, 2021-05-13)
cjc-gh-pages/code/04.PythonCrawler_netease_music.ipynb (49297, 2021-05-13)
cjc-gh-pages/code/04.PythonCrawler_selenium.ipynb (53032, 2021-05-13)
cjc-gh-pages/code/05.PythonCrawler_tianya_threads.ipynb (498577, 2021-05-13)
cjc-gh-pages/code/06.data_cleaning_Tweets.ipynb (120836, 2021-05-13)
cjc-gh-pages/code/07.data_cleaning_occupy_central_news.ipynb (54663, 2021-05-13)
cjc-gh-pages/code/08.01-statistics_thinking.ipynb (140758, 2021-05-13)
cjc-gh-pages/code/08.02-linear_algebra.ipynb (41866, 2021-05-13)
cjc-gh-pages/code/08.03-probability.ipynb (144372, 2021-05-13)
cjc-gh-pages/code/08.04-hypothesis_inference.ipynb (130902, 2021-05-13)
cjc-gh-pages/code/08.05-gradient_descent.ipynb (37615, 2021-05-13)
cjc-gh-pages/code/08.06-regression.ipynb (180144, 2021-05-13)
cjc-gh-pages/code/08.06-statsmodels.ipynb (45483, 2021-05-13)
cjc-gh-pages/code/08.07-analyzing_titanic_dataset.ipynb (569976, 2021-05-13)
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cjc-gh-pages/code/09.01-machine-learning-with-sklearn.ipynb (683237, 2021-05-13)
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cjc-gh-pages/code/09.06-Linear-Regression.ipynb (265776, 2021-05-13)
cjc-gh-pages/code/09.07-Support-Vector-Machines.ipynb (799730, 2021-05-13)
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*** *** # 《计算新闻传播学》 *** *** - 授课人：[王成军](http://chengjun.github.io) - 联系方式：wangchengjun@nju.edu.cn - 计算传播网：http://computational-communication.com - 在线讲义: https://computational-class.github.io/ccbook/ # 时间安排 - 36学时，两学分 | 时间 | 上午 | 下午 | 晚上 | 课时数量 | | -------------|:-------------:|:-------------:|:-------------:|-----:| | 2021-05-14 周五 | 9:00-12:00 | 14:00-17:00 |18:30-20:00 作业&答疑 | 6学时| | 2021-05-15 周六 | 9:00-12:00 | 14:00-17:00 |18:30-20:00 作业&答疑 | 6学时| | 2021-05-16 周天 | 9:00-12:00 | 14:00-17:00 | | 6学时| | 2021-05-21 周五 | 9:00-12:00 | 14:00-17:00 |18:30-20:00 作业&答疑 | 6学时| | 2021-05-22 周六 | 9:00-12:00 | 14:00-17:00 |18:30-20:00 作业&答疑 | 6学时| | 2021-05-23 周天 | 9:00-12:00 | 14:00-17:00 || 6学时| # 授课计划 - 一、[计算新闻传播学简介](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/01.intro2cjc.ipynb#) [[课程要求](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/0.about2cjc.ipynb#/)、 [常见问题](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/0.common_questions.ipynb#/) 、[Jupyter Notebook使用](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/01.jupyter_notebook.ipynb#/)、 [Slides制作方法](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/01.slides.ipynb#/)] - 二、[大数据简介 ](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/02.bigdata.ipynb#/) - 三、[数据科学的编程工具：Python使用简介（3h）](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/03.python_intro.ipynb#/) [[Graphlab](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/03.graphlab.ipynb#/)、[rpy2](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/03.rpy2.ipynb#/)] - 四、数据抓取：[抓取政府工作报告](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/04.PythonCrawlerGovernmentReport.ipynb#/) [[Beautifulsoup](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/04.PythonCrawler_beautifulsoup.ipynb#/)] - 五、数据抓取：[抓取天涯论坛帖子](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/05.PythonCrawler_tianya_threads.ipynb#/) - 六、数据清洗：[清洗推特数据](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/06.data_cleaning_Tweets.ipynb#/) - 七、数据清洗：[清洗占中新闻、清洗天涯论坛帖子](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/07.data_cleaning_occupy_central_news.ipynb#/) - 八、统计初步: [分析天涯论坛的帖子](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/08.analyzing_tianya_thread_network.ipynb#/) - 九、机器学习: [使用sklearn建立机器学习模型](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/09.01-machine-learning-with-sklearn.html#/) - 十、[文本挖掘简介](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/10.text_minning_gov_report.ipynb#/) - 十一、文本挖掘：[基于机器学习的情感分析](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/11.sentiment_classifier.ipynb#/) - 十二、文本挖掘：[主题模型](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/12.topic_models.ipynb#/) [[graphlab](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/12.topic-models-with-graphlab.ipynb#/)] - 十三、计算传播应用：[推荐系统简介](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/13.recsys_intro.ipynb#/) - 十四、计算传播应用：推荐系统实践 [[音乐推荐](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/14.millionsong.ipynb#/)、 [电影推荐](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/14.movielens_recommendation-systems.ipynb#/)、[隐含语义模型](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/14.matrix-factorization-demo.ipynb#/)] - 十五、[网络科学理论简介](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/15.network_science_intro.ipynb#/) - 十六、[网络科学模型](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/16.network_science_models.ipynb#/) - 十七、[网络科学：使用NetworkX分析网络结构](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/17.networkx.ipynb#/) - 十八、课程总结 [回帖网络分析](https://nbviewer.jupyter.org/format/slides/github/computational-class/cjc/blob/gh-pages/code/18.network_analysis_of_tianya_bbs.ipynb#/) # 作业信息 https://github.com/computational-class/cjc/wiki/ # Jupyter Notebooks & Tutorials 1. 下载后，打开code文件夹浏览 2. 【推荐】通过nbviewer浏览，打开http://nbviewer.jupyter.org/github/computational-class/cjc/blob/gh-pages/code/ ，选取需要浏览的Jupyter Notebook, 例如：[数据抓取：抓取47年政府工作报告](http://nbviewer.jupyter.org/github/computational-class/cjc/blob/gh-pages/code/04.PythonCrawlerGovernmentReport.ipynb) # Slides 1. 【推荐】通过nbviewer浏览，打开http://nbviewer.jupyter.org/github/computational-class/cjc/blob/gh-pages/code/ ，选取需要浏览的slides，点击上方的**view as slides**图标 2. 下载后，打开slides文件夹浏览

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