This is an open source text book for communication students to learn Python and data skills. No prerequisites, like prior programming or technology experience, are required. However, expect intensive training and quest in solving practical problems in media and communication fields. This repo collects lecture notes, lab exercises and data sets.

You can get started from Week 00. It walks you through our learning environment so you know how to find learning materials and seek for help effectively. When you encounter any problem, please use GitHub search function first. For example, you can search for "encoding" in this repo. If still unsolved, feel free to ask in the issue tracker. Last but not least, you are welcome to contribute to this ever growing open source book. Interested readers please check out Guide for contributors.

We expect the student in the digital era never set any limit for themselves and grow to a T-shape talent.

• Python and Data Analysis for Communication Students
  • Overview
  • Weekly Notes
  • FAQ Catalogue
    • General FAQs
    • Python Modules FAQs
    • Other Uncategorized FAQs
  • Appendix
  • License

• Introduction

• Notes: Week 00 - GitHub and markdown
• Notes: Week 01 - Terminal, Python, Jupyter Notebook
• Notes: Week 02 - Python as a powerful caculator: basics and arithmetics
• Notes: Week 03 - Control flow
• Notes: Week 04 - Data Structure
• Notes: Week 05 - Serialization: File, CSV, JSON
• Notes: Week 06 - API
• Notes: Week 07 - Get semi-structured data - Web scraping
• Notes: Week 08 - Advanced scraping: anti-crawler, browser emulation and other nitty gritty
• Notes: Week 09 - Work with table: data cleaning and pre-processing
• Notes: Week 10 - Work with table: 1D analysis and 2D analysis
• Notes: Week 11 - Present findings: data visualization and reproducible report
• Notes: Week 12 - Text data
• Notes: Week 13 - Datetime and Time Series
• Notes: Week 14 - Network data
• Notes: Week 15 - Geographical data
• Notes: Week 16 - High Dimensional Data
• Notes: Week 17 - Machine learning primer: clustering, classification, regression
• Notes: Week 18 - Python Engineering and Data Engineering

You can search through our whole repo, including all the notes and FAQs using the built-in GitHub search function. For example, you can search for "encoding".

• Course Admin
• Grading Scheme
• Guide for Contributor
• Projects Guideline
• Setup Python Environment on Windows and MAC
• Shell
• Python Language Basics
• Python 2 v.s. Python 3
• Dataprep
• Pro Tips
• Resources
• Guide for contributor
• GitHub
• HTML
• Encoding
• pip
• Computational Thinking
• File I/O
• Leetcode

• module: geopy
• module: requests
• module: csv
• module: BeautifulSoup
• module: jupyter
• module: pandas
• module: seaborn
• module: matplotlib
• module: lxml
• module: python-twitter
• module: datetime
• module: selenium
• module: wordcloud
• module: geopandas

• Other Frequently Asked Questions

• Spring 2018 students
• Fall 2018 students

CC-BY-NC-ND

List of contributors