Quantitative Social Science: Big Data and AI
Learning to use data to explore the economic,political and social world in the era of Big Data and AI.
Instructor
- Prof. Zhaopeng Qu
- qu@nju.edu.cn
- F203
Time and Location
- Tuesday and Thursday
- 09:50am–11:20am
- C203
News
Important
Welcome to the course website for Quantitative Social Science in the era of Big Data and AI (QSSBA). You can find everything about the course.
Course Description
In the digital age, Big Data and Artificial Intelligence (AI) are transforming the landscape of quantitative social science research. New data sources—ranging from text and images to videos and social media—combined with advanced AI tools and large language models, provide unprecedented opportunities to address complex social phenomena through data-driven approaches.
This introductory course provides students with essential skills for modern quantitative social science. It bridges traditional methods and emerging technologies, encompassing foundational statistical analysis, machine learning techniques, and the integration of diverse data sources, such as social media, satellite imagery, and historical documents. Students will gain hands-on experience in leveraging AI tools to enhance research efficiency while exploring the practical applications of quantitative methods in tackling real-world social questions.
Designed for beginners in quantitative research, the course emphasizes conceptual understanding and practical skills over mathematical complexity. By the end of the course, students will have a solid foundation in quantitative social science research methods and the ability to apply these methods in innovative and impactful ways.
By completing this course, students will:
Develop a strong foundation in the principles and methods of quantitative social science research.
Acquire practical skills in data collection, cleaning, analysis, and visualization using R and Python.
Gain experience with version control and modern AI tools (Like Github Copilot/ChatGPT/Claude/Gemini and Baidu) to enhance research efficiency.
Explore and analyze diverse data sources, including satellite imagery, web content, and historical documents, while developing expertise in text analysis, GIS mapping, and optical character recognition (OCR).
This course provides students with the tools and knowledge to navigate the rapidly evolving field of quantitative social science, preparing them to harness the power of Big Data and AI in both academic and professional contexts.
Acknowledge
This course has greatly benefited from materials shared by Scott Cunningham, Ed Rubin, and Andrew Heiss. More specifically, the course website is inspired by Matthew Blackwell’s website.