These courses are made available to you depending on faculty members' interest and availability. They are taught in English.

All hands-on sessions take place in the faculty's computer lab ("grosser PC-Raum", S18, HG, WWZ).

Pre-requisites apply only if you are working on your own computer. Otherwise, all software is already installed on the computers of the university.

If you are interested in attending a course, please register by sending an e-mail of interest to the responsible tutor no later than Sunday, September 21st, unless stated otherwise. Registration helps us determine the demand for each course.

• R
  • Contact: Axel Pillard (axel.pillard@unibas.ch)
  • No registration needed
  • Lecures: September 22nd and 29th (Mondays), 14:15 to 16:00
  • Software: R (https://www.r-project.org/) and Rstudio (https://posit.co/downloads/)
  • Course material: via OLAT (https://lms.uzh.ch/auth/RepositoryEntry/16607510589/CourseNode/99821620384198)
• Python
  • Contact: Léo Picard (leo.picard@unibas.ch)
  • Lectures: September 26th and October 3rd (Fridays), 8:15 to 10:00, ONLINE (zoom link will be provided after registration)
  • Software: Spyder, available on Anaconda (https://www.anaconda.com/download)
  • Course material: via GitHub (https://github.com/leops95/intro_to_python)
• Stata
  • Contact: Dr. Matthias Krapf (matthias.krapf@unibas.ch)
  • Lecture: September 26th (Friday), 14:15 to 18:00
  • Software: Stata (https://www.stata.com)
  • Course material from previous presentations here
• LaTeX
  • Contact: Felix Schulz (felix.schulz@unibas.ch)
  • Lecture: September 25th (Thursday), 14:15-16:00
  • Software: Overleaf, please set up a (free) account (https://www.overleaf.com)
  • Course material available here
• Julia
  • no lecture this term
  • past course material available here
• Matlab
  • no lecture this term
  • course material from previous presentations here 

• Causal Inference:

  • The Mixtape (by Scott Cunningham): https://mixtape.scunning.com/
    “Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.”

• R

  • Hands-On Programming with R: https://rstudio-education.github.io/hopr/

  • R for Data Science (2^nd edition): https://r4ds.hadley.nz/

  • Mastering Shiny: https://mastering-shiny.org/index.html

  • Swirl, learn R in R: https://swirlstats.com/

• Python

  • Logging in Python: https://realpython.com/python-logging/

  • Working with Python’s built-in exceptions: https://realpython.com/courses/working-builtin-exceptions/

  • Text processing: https://realpython.com/natural-language-processing-spacy-python/

  • Image processing: https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html

  • Machine learning: https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about

• Stata

  • Coefficient plots: https://repec.sowi.unibe.ch/stata/coefplot/getting-started.html

  • Binned scatterplots: https://michaelstepner.com/binscatter/

  • Multi-way fixed effects estimation: https://www.stata.com/meeting/chicago16/slides/chicago16_correia.pdf

  • Difference-in-Differences with Multiple Time Periods in Stata: https://www.stata.com/meeting/us21/slides/US21_SantAnna.pdf

  • Automated detection of Stata commands linked to common reproducibility failures https://www.stata.com/meeting/us25/slides/US25_San_Martin.pdf

• Code equivalencies

  • Stata ↔ Python https://www.danielmsullivan.com/pages/tutorial_stata_to_python.html

  • R ↔ Python https://www.mit.edu/~amidi/teaching/data-science-tools/conversion-guide/r-python-data-manipulation/

  • R ↔ Stata https://clanfear.github.io/Stata_R_Equivalency/docs/r_stata_commands.html