Office hours
Tuesdays and Wednesdays 14.00-15.00 in office D/N/117B.
Drop in or see the "Contacts" tab on your VLE course page to book a slot.
There is no drop in outside of term time.

University of York
2022 - 2023:

• Political Enquiry (undergraduate)
  Lectures and labs on social science data analysis. Module convenor, single term.
• War & Peace (undergraduate)
  Seminar on IR theories and peace & conflict. Co-taught, full year.
• Causes and Conduct of Conflict (postgraduate)
  Seminar on peace & conflict studies. Co-taught, single term.
  

University of the German Federal Armed Forces Munich
2021:

• Conflict Prediction: Theory and Methodology (postgraduate)
  Advanced seminar on peace & conflict studies and machine learning (R). Co-taught, single term.
  
• Conflict Prediction: Theory and Methodology (undergraduate)
  Introductory tutorial on peace & conflict studies and machine learning (R). Co-taught, single term.
  

University of Mannheim
2020:

• Militias, Militaries, and Peacekeepers: Structure, Effectiveness, and Civil-Military Relations (undergraduate)
  Seminar on the role of security forces in conflict. Course convenor, single term. [Evaluations]
  

University of Essex
2018 - 2019:

• Violent Non-State Actors: Violence, Conflict and Crime (undergraduate)
  Lectures on terrorism, cartel violence, and private military firms. Co-taught, single term. [Evaluations]
• Advanced Methods (postgraduate)
  Seminar on basic and advanced econometrics (Stata & R). GTA, full year. [Evaluations]
• Political Explanation (postgraduate)
  Seminar on basic and advanced topics in social science data analysis (R). GTA, full year. [Evaluations]

2017 - 2018:

• Advanced Methods (postgraduate)
  Seminar on basic and advanced econometrics (Stata & R). GTA, full year. [Evaluations]

Essex Summer School in Social Science Data Analysis
2021:

• Introduction to R
  Boot camp seminar on data management, visualization, and programming (R). Course leader.
  

2020:

• Introduction to R
  Boot camp seminar on data management, visualisation, and programming (R). Course leader.
  
• Introduction to Quantitative Methods in R
  Two-week course on data management, analysis, and visualisation (R). GTA.  [Evaluations]
  Course leader: Johannes Karreth.
  

2019:

• Advanced Quantitative Data Analysis
  Two-week course on methods of causal inference and prediction (Stata). GTA.  [Evaluations]
  Course leader: Moritz Marbach.
  

2018:

• Advanced Quantitative Data Analysis
  Two-week course on methods of causal inference and prediction (Stata). GTA.  [Evaluations]
  Course leader: Moritz Marbach.
  

Workshops
2021:

• Quantitative Data Management and Analysis in R, German Youth Institute (DJI).
  Research staff training on data management and analysis (R). Course leader. [Evaluations]
  

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Learning resources

Below I list a few helpful third-party materials on statistics. Please let me know should you encounter a broken link.

Math refreshers:

• "Math for Political Scientists" by J. Alexander Branham.
• Khan Academy has some general video lessons on basic and advanced math.
• Specifically for political scientists, Havard offers a booklet covering basic and advanced mathematical content (and some R and LaTeX).
• David Siegel provides a comprehensive math camp with video explanations.

Statistical theory:

• Seeing Theory provides visualisations of core statistical concepts.
• Learning Statistics with R is a book project by Danielle Navarro, mainly directed at psychology students (but broadly applicable).
• Youtube channel Applied Methods by Paul Goldsmith-Pinkham.
• Loss Data Analytics is an open textbook project covering many data science topics using R. It is written by and for the actuarial science community, but its content is broadly applicable.
• Lecture slides on "Advanced Quantitative Research Methodology" (various topics) by Gary King.
• Comprehensive information and interactive materials on "The Fallacy of Placing Confidence in Confidence Intervals" by Richard D. Morey and others.
• Some more resources for learning Bayesian on LearnBayes.
• A visual explanation of Markov Chains by Victor Powell and Lewis Lehe.
• Online access to the book on machine learning & supplementary materials by Hastie et al. (2009).
• Two great books on causal inference: The Effect by Nick Hufington-Klein and Causal Inference: The Mixtape by Scott Cunningham.
• The Machine Learning University (MLU) has several small and very accessible tutorials on basic statistical concepts.
  

Learning R:

• If you are new to R and RStudio, follow these instructions on Swirl.
• Essential R resources and further links are on Gary King's "Advanced Quantitative Research Methodology" website.
• Under the heading "Teaching", Garrett Glasgow has many neat code chunks on basic and advanced topics.
• For a steep learning curve, Kevin Shoemaker provides materials for an R bootcamp.
• The primers on the RStudio Cloud provide for interactive learning of basics and the tidy dialect.
• Andrew Heiss has a great course website for causal inference.
• A rich online resource listing many different & mostly free books on R, kicked off by Oscar Baruffa.
• The bookdown project with many excellent books related to R and data science.
• For learning Stan, the official Stan User's Guide is very accessible and comprehensive.

Learning Stata:

• Two good and easy-to-follow introductions to Stata are provided by Ista Zahn at Havard and Germán Rodríguez at Princeton.

LaTeX materials:

• "The Not So Short Introduction to LaTeX" has pretty much everything to get started.
• For a more interactive experience: Overleaf resources with this 30-minute intro.
• Extensive documentation is offered by Wikibooks and by the Havard Mathematics Department.