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.

Spring 2025
Hopkins-Nanjing Center
Nanjing University and Johns Hopkins University

Instructor

Teaching Assistant

  • Zhengwu Raoxue
  • xr2075@nyu.edu
  • WeChat ID: aCyberPanda

Time and Location

  •   Tuesday and Thursday
  •   09:50am–11:20am
  •   C203

Course Logo

News

Important
  • 4/21: Tomorrow, we will discuss Hypothesis Testing and Confidence Intervals in OLS Regression. The lecture slides are available for download from the schedule page here. I will also reserve some time to hear your brief oral presentations about your research topics. The oral presentation of evaluation will be conducted on following lectures. I also upload some samples for research presentations by students in Business School, Nanjing University for your reference here.

  • 4/17: Tomorrow, we extend the simple OLS Regression to multiple regression. The lecture slides are available for download from the schedule page here.

  • 4/15: This week, we will explore one of the most fundamental and powerful tools in causal inference: OLS Regression. The lecture slides are available for download from the schedule page here.

  • 4/8: Tomorrow, we will begin learning about Causal Inference, which is one of the fundamentals of Quantitative Social Science research. The slides can be downloaded from the schedule here. Additionally, the deadline for team formation has been extended to tomorrow. If you’re experiencing any difficulties joining a team, please inform me or our TA.

  • 3/27: Tomorrow, we will continue with R Lab 4 on Making Tables in the first session. And then we will spend the rest of the time on RMarkdown. The slides and a .rmd file can be downloaded from the schedule here.

  • 3/24: Tomorrow, we will continue with R Lab 4 on Data Management (III). Hopefully, we will be able to complete the lab of R Basics in this week. The slides and Rscript can be downloaded from the schedule here.

  • 3/20: Tomorrow, we will continue with R Lab 3 on Data Management (II). Hopefully, we will be able to complete the lab. The slides and Rscript can be downloaded from the schedule here.

  • 3/18: Tomorrow, we will continue the R Lab2 in Data Management(I). And then spend the rest of the time on Data Management(II). The slides and Rscript can be downloaded from the schedule here.

  • 3/13: Tomorrow, we will continue the lecture on measurement in social science research. And then spend the rest of the time on basic data management using R. The slides and Rscript can be downloaded from the schedule here.

  • 3/11: In the first part of the lecture, I will introduce measurement in social science research. The second part will be dedicated to basic data management using R. You can download the slides from the schedule here.

  • 3/6 I will continue to introduction to R and RStudio. This time we will try a more accessible way to learn R by using swirl and several other amazing packages. On the second part of the class, I will introduce the basic concepts of LLMs and how to use them to help us coding in RStudio.If we still have time, I will introduce the basic concepts of Git and how to use it with Github in RStudio. The updated version of the slides including the new content can be downloaded from the schedule here.

  • 3/3 Tomorow I will finish the rest of the slides for the first lecture.And then I will introduce the toolkits for the course including R, RStudio, Git, Github, and AI tools for the rest of the time. The slides can be downloaded from the schedule here.

  • Tomorrow I will finish the content of the first lecture which I did not finished in Tuesday in the first 40 mins.(Note: the slides are updated.You could download the new slides from the schedule here If we still have time, I will introduce the Basic R and RStudio for the second 40 mins.

  • Tomorrow marks the first lecture of the course. I have prepared a few slides for this session, which you can download from the schedulehere. I look forward to meeting you all tomorrow.

  • 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.