Teaching at the Institute of Econometrics and Data Science

Area of Expertise in Business Administration and Economics

• Bachelor Seminar on Data Science & Econometric Methods (273034)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme, Sönksen
  Contents:	Students learn how to write a scientific paper in the fields of data science or econometrics and how to present research results in a convincing and scientifically accurate manner. Students deepen their methodological skills and are prepared for writing their final theses.
  Comments:	Details regarding registration procedures and the schedule will be announced on the institute's website. Examiner: Prof. Dr. Sönksen

• Introduction to the Scientific Work at the Institute of Econometrics and Data Science (273035)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme
  Contents:	Introduction to Programming with Python Introduction to LaTeX Academic Writing and Integrity

• Introduction to Machine Learning (273037)

  	Time and room:	Lecturer:
  	Thu. 09:15 - 10:45 | I-301	Sönksen
  Contents:	This course provides students with an introduction to machine learning methods. It deals with regression and classification settings and covers the following topics: difference between econometrics and machine learning learning theory (e.g. bias-variance trade-off) local prediction methods shrinkage and feature selection techniques classification and regression trees
  Literature:	Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)

• Exercise Introduction to Machine Learning (273038)

  	Time and room:	Lecturer:
  	Fri. 09:15 - 10:45 | I-301	Itzen

Area Data Science and Applied Econometrics

• Introduction to the Scientific Work at the Institute of Econometrics and Data Science (373030)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme
  Contents:	Introduction to Programming with Python Introduction to LaTeX Academic Writing and Integrity

• Master Seminar on Data Science & Econometric Methods (373031)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme, Sönksen
  Contents:	Students learn how to write a scientific paper in data science or econometrics and to present their results convincingly and in a scientific way. Students deepen their methodological skills and are prepared for writing their master's theses.

• Causal Machine Learning (373032)

  	Time and room:	Lecturer:
  	Fri. 09:15 - 10:45 | II-013	Sönksen
  Contents:	This course combines the flexibility of machine learning methods with econometrics and covers the following topics: a short overview of machine learning measures of causal effects (e.g., average treatment effects) machine learning techniques to estimate causal effects (e.g., causal forests)
  Literature:	Applied Causal Inference Powered by ML and AI, Chernozhukov/Hansen/Kallus/Spindler/Syrgkanis

• Advanced Predictive Methods (373033)

  	Time and room:	Lecturer:
  	Thu. 11:00 - 12:30 | I-332	Sönksen
  Contents:	This course familiarizes students with more elaborate prediction techniques. It covers the following topics: learning theory random forests boosting and bagging artificial neural networks support vector machines
  Literature:	Elements of Statistical Learning (Friedman & Tibshirani)

• Exercise Advanced Predictive Methods (373034)

  	Time and room:	Lecturer:
  	Fri. 11:00 - 12:30 | II-013	Sönksen

• Finance Research Seminar (77782)

  	Time and room:	Lecturer:
  	Wed. 11:00 - 12:30 | I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
  Contents:	External guests present their latest research