社会数据分析，Python应用入门
课程教师
Blake Miller
教师简介
Blake Miller is an Assistant Professor of Political Science at the University of Pennsylvania. They are also affiliated with the Center for the Study of Contemporary China. Blake's research examines how states control media, the internet, and civil society organizations. They are currently working on a book manuscript on information control. Blake received their PhD in Political Science and Scientific Computing from the University of Michigan. Prior to joining Penn, they were an Assistant Professor of Computational Social Science at the London School of Economics and Political Science, and a Post-Doctoral Fellow at the Dartmouth College Program in Quantitative Social Science.
课程内容
The abundance of large-scale data and the computational tools to analyze them have transformed social science research, dramatically broadening the scope of social scientific enquiry. This course covers a variety of computational approaches to studying human behavior from text data, digital traces of behavior, social media data, and other sources. Each class will be broken into two main sections. First, we will introduce examples of computational social science research, outlining key methodological strategies, challenges, and best practices for researchers. Second, we will cover basics of computer programming and data management necessary for carrying out one’s own computational social science research. The course will introduce basic coding in Python, but no prior coding experience will be necessary.
预期目标
to introduce students to important concepts and methodologies related to computational social science research
to teach students practical concerns and best practices for data management and data collection
to build foundational skills necessary to construct useful datasets for their research from unstructured, semi-structured, and secondary data
to build a roadmap for continued learning through promoting awareness of more advanced and specialized tools and where to look for problem-solving/reference.
课程安排
Lecture | Topic (2.5 teaching hours) |
1 | Course Intro, What is Computational Social Science? |
2 | Survey of Computational Social Science Research |
3 | Basic Programming: Variables, Operators |
4 | Basic Programming: Conditionals, Loops |
5 | Basic Programming: Functions |
6 | Data Types and Data Wrangling |
7 | Data and Research Best Practices |
8 | Text Data |
9 | Social Media Data and APIs |
10 | Gathering Data from the Web |
Textbooks and Course Materials
Readings for each session are detailed below.
Textbooks: Think Python: How to Think Like a Computer Scientist, 2nd Edition (English, Chinese)
Other resources:
Required Software
This course is taught in Python, using Python 3. You will need to have Python 3 installed on your computer and bring it to class each session. If you have not yet installed Python 3, you will need to do so. Please use the following resources for installing Python 3 on your machine:
For Windows users:
Install Sublime Text from the official Sublime Text website.
Install Python 3 (click “Latest Python3 Release”)
Open Git Bash and run python3 --version to verify Python 3 is accessible. If that does not work, try python --version.
For Mac users:
Open Terminal on Mac (Applications → Utilities → Terminal).
Install Xcode Command Line Tools by running xcode-select --install in Terminal.
Install homebrew by pasting the installation command from the website into Terminal. Alternatively download the installer here.
Install Sublime Text from the official Sublime Text website and follow the installation instructions.
Use homebrew to install Python 3 by running brew install python in Terminal.
Run python3 --version in Terminal to verify Python 3 is accessible.
Grading
Quizzes (60%, in class): There will four in-class quizzes. The quizzes will test knowledge of the material covered in the previous classes and readings.
Final Problem Set (40%, due July 19): You will have one final problem set to apply all of the things we learned. For the problem set, you will analyze a dataset we will discuss in class in the final lectures.
