Teaching Philosophy
I believe that rigorous quantitative training and hands-on data experience empower students to tackle real-world economic questions. My goal is to create an inclusive classroom where every student builds solid foundations in econometric theory while gaining practical skills in R and Python. I emphasise active learning combining lectures with data workshops and group problem sets to ensure that concepts “click” and students gain confidence with code and empirical methods. I incorporate examples from the Global South to ensure that the students have the toolkit to analyse the real world around them.
Econometrics (UG)
• Core introduction to inference and OLS regression.
• Weekly problem classes where students apply theory to R exercises.
• Emphasis on interpreting output in policy contexts
Econometrics with R Coding (PGT)
• Covers multiple regression, heteroskedasticity, and basic panel data in R.
• Lab-style seminar sessions: each week students write scripts to run regressions, generate plots, and interpret results.
• Assignments include real world econometric analysis with applications in the context of local economies
Advanced Econometrics with R Coding (PGT)
• Builds on first semester econometrics: instrumental variables, difference-in-differences, limited-dependent models, regression discontinuity design
• Hands-on labs: students estimate two-stage least squares, and implement treatment-effects techniques in R.
• Final project requires students to replicate a real world applied published paper.
Data Analytics with Python (PGT)
• Introduction to Python for data cleaning, visualisation, and basic statistical modelling (Pandas, NumPy, Matplotlib)
• Emphasis on reproducible workflows: commenting code, and producing Jupyter notebooks suitable for collaborative research.
Supervision & Student Engagement
Dissertation Supervision
• I advise final-year Economics students in their end of year project.
• I supervise postgraduate students (currently 4).
Academic Advising & Office Hours
• I hold two 60-minute office sessions per week for questions on coursework, careers, and research ideas.
• As Department Convenor for Student Engagement, I coordinate informal Student-Staff Forum each month whereby student representatives can raise any ongoing concerns with me.
Previous Teaching Experience
University of Sussex (2018–2023)
Doctoral Tutor, Economics & IDS
• Led small-group seminars in Macroeconomics 1, Microeconomics 2, and Introduction to Quantitative Methods.
• Taught “Economic Perspectives on Development” (IDS) and “Global Economic Issues” (Summer School).
• Developed beginner Python workshops (data cleaning, basic plotting) and a STATA bootcamp (data management, simple regressions).
Summer Workshops & Bootcamps
Designed and ran weekend “Python for Social Scientists” sessions, introducing Pandas and basic visualisation.
Organised STATA courses covering: data import, variable creation, loops, and regression diagnostics.
Feel free to email me ( ab185@soas.ac.uk ) if you have any questions about course content, research ideas, or further reading.