‘19 MACNM Computational Workshop

Computational Workshop for MACNM Alumni

  Live broadcast is available on Douyu: https://www.douyu.com/6226461.   Topics: Date | 9:00-12:00 | 14:00-17:00 | Evening
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Week 1 | | |
Jan 5 Saturday | 0. Introduction (Lecturer: Jonathan Zhu) [Notebook] 1. Python Programming I (Lecturer: LAN Ji) [Notebook] [Video a] [Video b] | 2. Python Programming II (Lecturer: CHEN Zhicong) [Notebook] [Video_a][Video_b][Video_c] | Practice
Jan 6 Sunday | 3. Web Scraping (Lecturer: GUAN Lu) [Notebook][Video_a][Video_b] [Video_c][Video_d] | 4. Data Visualization (Lecturer: LAN Ji) [Notebook] [Video_a][Video_b] [Video_c] [Video_d] | Practice
Week 2 | | |
Jan 12 Saturday | 5. Text Mining (Lecturer: GUAN Lu) [Notebook][Video_a][Video_b] [Video_c][Video_d] | 6. User Profiling (Lecturer: CHEN Zhicong) [Notebook][Video_a][Video_b] [Video_c] | Practice
Jan 13 Sunday | 7. Network Analysis (Lecturer: Jonathan Zhu) [Notebook][Merged_Video] | 8. A/B Test (Lecturer: Jonathan Zhu) [Notebook][Merged_Video] | Practice
  Data and Home Exercise

• Data: [Link]
• Home Exercise for Week 1: [Link] [Answer]
• Home Exercise for Week 2: [Link] [Answer]

The course includes a 2-hour lecture and a 1-hour practice each topic. Web-based tools will be used whenever possible and appropriate to facilitate the distribution of class materials and the interaction among instructors and students. Venue: M5505, 5/F, Run Run Shaw Creative Media Centre, 18 Tat Hong Avenue, City University of Hong Kong, Kowloon Tong, Hong Kong   Preparation:

• Anaconda Installation (including both Python and Jupyter Notebook)
  1. Anaconda
  2. Make sure Anaconda-Jupyter can run (See an instruction)
• Apply Twitter Api
  1. Apply Twitter developer App at https://developer.twitter.com/en/apps
  2. Fill the information requirement (user profile, account details, use case details) and finish email verification
• Python Learning on DataCamp[Link]
  1. Go through the video and exercises (Total 4 hours)
• Introduction to Computational Methods
  1. How to learn computational communication research (JZ’s slide)
• Optional Learning Materials
  1. Interactive learning without installation: w3schools
  2. Video course-Learn Python with Socratica [Youtube] [Bilibili]
  3. Book-Python for Everyone
  4. Jupyter Notebook Documentation and Tutorial

1. 张伦，王成军，许小可. (2018). 计算传播学导论. 北京师范大学出版社 (JD.com)

1. Python learning on DataCamp and w3schools

1. Pandas Documentation: https://pandas.pydata.org/pandas-docs/stable/
2. Pandas Cookbook: https://github.com/jvns/pandas-cookbook
3. Pandas Lessons: https://bitbucket.org/hrojas/learn-pandas

1. Liang, H., & Zhu, J. J. H. (2017). Big data, collection of (social media, harvesting). In J. Matthes, C. S. Davis, & R. F. Potter (Eds.), The International Encyclopedia of Communication Research Methods. NJ: Wiley-Blackwell.

1. Python package: seaborn

1. 第二章 文本分析简介. In张伦，王成军，许小可. (2018). 计算传播学导论. 北京师范大学出版社

1. 刘鹏. (2015). 计算广告: 互联网商业变现的市场与技术. 人民邮电出版社.
2. 项亮. (2012). 推荐系统实践. 人民邮电出版社. Link
3. Tan, P. N., Steinbach, M., & Kumar, V. (2013). Introduction to data mining.

1. Easley & Kleinberg. (2010). Networks, crowds, and markets
2. Hanneman & Riddle. (2005). Introduction to social network methods

1. Gephi (https://gephi.org/users/download/)
2. Python package: NetworkX

1. Matthew Salganik (2018). Bit by bit: Social research in the digital age. Chapter 4. Running experiments. Available online.