# Teaching

• #### Statistics for the Social Sciences

• This class covers the topics that I find most interesting at an introductory level in statistics and data science. It includes R, R markdown, and GitHub websites, linear regression, hypothesis testing, and causal inference. Here is a sample of some of the lessons and assignments I gave the students.
• Course description: Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data. Statistical problems can be thought of as problems of missing data: If we do not get to see the entire picture, can we (and should we) generalize results from a small sample to a population? This course will be an initial exposure to statistics through the lens of careful and systematic data analysis. It will train you to ask questions about how a dataset was collected, what to do when you first access a dataset, and how to think about what analysis you will carry out to answer a research question. You will not be told to follow a procedure blindly, but instead you will be given the tools so you can decide which procedure should be followed. It is possible that the proper procedure is not possible, but with this training you will know why. It will also help you determine what assumptions are necessary to make claims and answer questions. After completing this course, you should be able to:
• Explore a dataset through visualization.
• Select and perform the appropriate statistical analysis for your study.
• Properly interpret the results of your analysis and communicate them to an audience.
• Recognize when you need help with designs or analyses beyond those discussed in this class.
• #### Criminal Justice

• Course description: Controversies about criminal justice often arise in public discourse. How can gun violence be reduced? Do body-worn cameras improve police behavior? What policies might reduce incarceration rates? How can the opioid epidemic be stopped? Are immigrants more likely to commit crimes than non-immigrants? Does the death penalty deter people from committing murder? To address these controversial questions, this course takes an evidence-based approach. This means examining successful data analyses when available, and understanding why they are successful, as well as understanding when the analyses or data are insufficient to answer the question. The course introduces the history and composition of the criminal justice system, and focuses especially on policing tactics, detention, trial, forensic science, punishment, re-entry, and crime prevention.
• #### Forensic Analysis

• Course description: This course discusses the need for stronger scientific foundations in the analysis of forensic evidence from a scientific and a policy perspective. Forensic evidence, such as fingerprints, firearms, and hair, has been analyzed for hundreds of years to inform crime investigations and prosecutions. However, recent advances, especially the use of DNA technology, have revealed that a faulty forensic analyses may have contributed to wrongful convictions. These advances have demonstrated the potential danger of information and testimony derived from imperfect analysis, which can result not just in wrongful convictions but also in errors of impunity. In this course, students learn about the history of forensics, as well as about the recent advances that aim to improve current practices. It is an interdisciplinary course, but it focuses mostly on the statistical and scientific aspects of testing in forensics. Students discuss recent solutions that quantify the uncertainty, limitations, and errors associated with human factors, pattern evidence, and digital evidence. No prior statistical or forensic knowledge is expected. The course will be useful for students who wish to become forensic practitioners, law enforcement officers, lawyers, judges, researchers, or simply informed citizens.