Brian Oduor is an Adjunct Assistant Professor of Public Service of NYU’s Robert F. Wagner Graduate School of Public Service. He designs data-driven intelligence. He’s the founder of Cap of X, a think-&-do tank that collaborates with organizations to efficiently augment machine intelligence with existing business functions in order to find new opportunities and enhance organizational ethos / bottom line.
Brian spent a decade using the capital markets to solve macro issues. As the youngest energy derivatives trader at Barclays, he was instrumental in the formation of the U.S’s first greenhouse gas market. He was also involved in developing the U.S. Virgin Island’s waste disposal market and in the unsuccessful creation of a sustainable charcoal market in Kenya, which would save its fragile water catchment areas.
In addition, Brian has advised media and tech companies facing disruption from emerging technologies such as the internet and mobile telephony. Covering over $30bn worth of deals at JPMorgan, Brian worked with newspaper, cable, telco and satellite companies to strategize and recapitalize for the future.
Brian holds a BA, summa cum laude, in Mathematics and Computer Science from Connecticut College, where he was a member of Phi Beta Kappa and Pi Mu Epsilon (National Mathematics Honor Society). Brian also holds two Masters degrees from Yale University, where he focused on Behavioral Economics and the Business of Climate Change.
This 7-week course exposes the students to the application and use of data analytics in setting public policy. The course does so by teaching introductory technical programming skills that allow students to learn and apply Python code on pertinent public policy data, while emphasizing on applicability. The course is accompanied by readings for each class in order to contextualize why data analytics supplements but doesn’t replace the student / professional role in setting public policy.
With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.
At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.