This course is Part I of the SBS in DC Internship Program. It is designed to help prepare students by equipping them with critical knowledge, skills and awareness of professional and personal development tools to enhance their internship experience. It is required of students participating in the SBS in DC Internship program. The class will build upon the participant's academic fields of interest, professional goals and assist with community development of the SBS in DC cohort.

This course provides foundational knowledge of the design, implementation, management, and evaluation of transportation systems in cities and regions from the perspective of urban planning. Topics include congestion, safety, health and environmental impacts, connections to land use, policy and governance, finance and funding, economic analyses, network modeling, public transit, bike and pedestrian travel, social equity, placemaking, and street design. A basic understanding of cities and planning is presumed.

This course is designed for business majors to be able to rapidly prototype software using modern LLMs. The course will focus on development of software that scrapes, cleans, and visualizes data using interactive frameworks such as Shiny and d3.js. Students should have a basic understanding of the fundamentals of AI in business as well as business intelligence and visualization, but prior programming expertise is otherwise not assumed.

Bayesian modelling approaches provide natural ways for researchers in many disciplines to structure their data and knowledge, and they yield direct and intuitive answers to scientific questions. This course introduces the principles and practice of Bayesian modeling. Students will learn the fundamental concepts of Bayesian inference and the roles of prior, likelihood, and posterior distributions. Students will gain an understanding of sampling-based computational approaches, including Markov Chain Monte Carlo (MCMC). Emphasis is placed on interpreting the results of Bayesian analyses.