I taught a large range of units at all levels for large and small classes. My main expertise in teaching lies in teaching statistics and econometrics units. Lately I have also been teaching a large first year Mathematics unit as well as a study skills unit for first year economics students.
Advanced Mathematics is a course which helps students develop the core mathematical tools they require to study on Economics units, namely the Lagrangian Method to tackle constrained multivariate optimisation problems as well as the skills to solve simple difference equations. It assumes that students have either done the equivalent of A-level Maths or had a prior mathematics course which taught students up to partial derivatives.
Advanced Statistics follows on from Advanced Mathematics and helps students to develop skills in descriptive and inferential statistics. In the course of learning these skills students also are supported in implementing any techniques taught in EXCEL as being able to navigate around a spreadsheet programme is a crucial skill for their further studies and live after University.
These units are structured in a way which delivers the actual content asynchronously through Mobius Lessons (technology provided by DigitalEd. This is effectively an online, interactive textbook, which attempts to interweave text-based delivery, with supplementary videos and short exercises which students tackle right after being presented new bits of content. This delivers immediate feedback opportunities for the learner. More detail on this here.
I teach this unit together with my colleague Martyn Andrews. Quantitative Methods is an econometrics unit with a clear focus on modern causal inference methods (e.g. diff-in-diff, regression discontinuity, IV) with a short introduction to time-series issues (mainly issues leading up to spurious regressions). This unit has an explicit empirical and practical focus and students are exposed to a significant amount of coding in R. A substantial part of the unit’s assessment is an empirical group project.
A major contribution of this unit is to put the programming (in R) at the center of the student’s learning. There are weekly scripts which allow students to replicate all the empirical work presented in the content sections of the unit (Examples: Card and Krueger Minimum Wage Example, Analysing Covid data).
In addition there are regular computer labs leading students through programming tasks. The main feature of these computer labs is that students are guided through the material without being given all the correct code. With appropriate scaffolding students are asked to complete code, adjust code, find errors in code, use the help functions and search on the internet for solutions. This is to help students develop programming skills which allow them to prosper outside the confines of the unit. (Examples: Lab 0, Lab 1, Lab 2)