2020 - 2021
This is a two-semester course that provides a rigorous introduction to methods and tools in advanced data analytics for social science doctoral students. It was developed as the required course for Berkeley's Computational Social Science Training Program. The goal of the course is toprovide students with a strong foundation of knowledge of core methods, thereby preparing them to contribute to research teams, to conduct their own research, and to enroll in more advanced courses. The course will cover research reproducibility, machine learning, natural language processing, and causal inference. The course is divided into modules, each lasting 3-5 weeks. Each module will include lectures, discussion ofexample research articles, lab exercises, and a group project involving Python or R programming. Projects, typically done in groups of 3 students, will also provide the opportunity to practice reproducibility techniques, data manipulation and transformation, and data science workflows.
Clients increasingly want their lawyers to understand their products and services on a technical level. Regulators need to understand how their rules will be implemented in code. Lawyers increasingly need tools to automate the process of collecting, organizing, and making sense of impossibly large troves of information.
Computer Programming for Lawyers introduces law students to the Python programming language with an emphasis on text analysis. For instance, we will use the same tools data scientists employ to "scrape" (collect) data, organize it, clean it, and use it to explore legally-relevant questions. This course will lay the foundation for understanding the basics of how companies leverage software engineering and “big data.” These skills have applications from legal discovery, to deposition preparation, to research into administrative or judicial action.
*Non-instructional role, primarily working on content development and logistics.
2018 - 2019
Data, Prediction and Law allows students to explore different data sources that scholars and government officials use to make generalizations and predictions in the realm of law. Students will apply the statistical and Python programming skills from Foundations of Data Science to examine a traditional social science dataset, “big data” related to law, and legal text data. See here for my GitHub repository that contains the in-class lab assignments and here for a blog post describing how we created an upper-level domain-emphasis course within data science.
This course teaches you to use the tools of applied historical thinking and Science, Technology, and Society (STS) to recognize, analyze, and shape the human contexts and ethics of data. It addresses key topics such as doing ethical data science amid shifting definitions of human subjects, consent, and privacy; the changing relationship between data, democracy, and law; the role of data analytics in how corporations and governments provide public goods such as health and security to citizens; sensors, machine learning and artificial intelligence and changing landscapes of labor, industry, and city life. It prepares you to engage as a knowledgeable and responsible citizen and professional in the varied arenas of our datafied world. See here for more information on the Berkeley data science undergraduate program, and the human contexts and ethics program specifically.
The course applies microeconomic theory analysis to legal rules and procedures. Emphasis will be given to the economic consequences of various sorts of liability rules, remedies for breach of contract and the allocation of property rights. The jurisprudential significance of the analysis will be discussed.