Nicolai Meinshausen: Catalogue data in Spring Semester 2023

Name	Prof. Dr. Nicolai Meinshausen
Field	Statistics
Address	Professur für Statistik ETH Zürich, HG G 23.2 Rämistrasse 101 8092 Zürich SWITZERLAND
Telephone	+41 44 632 32 74
E-mail	meinshausen@stat.math.ethz.ch
URL	http://stat.ethz.ch/~nicolai
Department	Mathematics
Relationship	Full Professor

Number

Title

ECTS

Hours

Lecturers

401-3620-22L

Student Seminar in Statistics: Causality

Number of participants limited to 76.
Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science.

4 credits

P. L. Bühlmann, N. Meinshausen

Abstract

Causality is dealing with fundamental questions about cause and effect. The student seminar covers statistical and mathematical aspects of causality ranging from fundamental formalization of concepts to practical algorithms and methods.

Learning objective

The participants of the seminar acquire knowledge about: concepts and formalization of statistical causality; methods, algorithms and corresponding assumptions for inferring causal relations from data; causal analysis in practice based on real data.

Prerequisites / Notice

Basic course in probability and statistics.

401-4620-00L

Statistics Lab

6 credits

M. Kalisch, M. Mächler, L. Meier, N. Meinshausen

Abstract

"Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It
provides a learning environment in a realistic setting.

Students lead a regular consulting session at the Seminar für Statistik
(SfS). After the session, the statistical data analysis is carried out and
a written report and results are presented to the client. The project is
also presented in the course's seminar.

Learning objective

- gain initial experience in the consultancy process
- carry out a consultancy session and produce a report
- apply theoretical knowledge to an applied problem

After the course, students will have practical knowledge about statistical
consulting. They will have determined the scientific problem and its
context, enquired the design of the experiment or data collection, and
selected the appropriate methods to tackle the problem. They will have
deepened their statistical knowledge, and applied their theoretical
knowledge to the problem. They will have gathered experience in explaining
the relevant mathematical and software issues to a client. They will have
performed a statistical analysis using R (or SPSS). They improve their
skills in writing a report and presenting statistical issues in a talk.

Content

Students participate in consulting meetings at the SfS. Several consulting
dates are available for student participation. These are arranged
individually.

-During the first meeting the student mainly observes and participates in
the discussion. During the second meeting (with a different client), the
student leads the meeting. The member of the consulting team is overseeing
(and contributing to) the meeting.

-After the meeting, the student performs the recommended analysis, produces
a report and presents the results to the client.

-Finally, the student presents the case in the weekly course seminar in a
talk. All students are required to attend the seminar regularly.

Lecture notes

n/a

Literature

The required literature will depend on the specific statistical problem
under investigation. Some introductory material can be found below.

Prerequisites / Notice

Prerequisites:
Sound knowledge in basic statistical methods, especially regression and, if
possible, analysis of variance. Basic experience in Data Analysis with R.

Competencies

Subject-specific Competencies	Concepts and Theories	fostered
	Techniques and Technologies	fostered
Method-specific Competencies	Analytical Competencies	fostered
	Decision-making	fostered
	Media and Digital Technologies	fostered
	Problem-solving	fostered
	Project Management	fostered
Social Competencies	Communication	fostered
	Cooperation and Teamwork	fostered
	Customer Orientation	fostered
	Leadership and Responsibility	fostered
	Negotiation	fostered
Personal Competencies	Adaptability and Flexibility	fostered
	Creative Thinking	fostered
	Critical Thinking	fostered
	Self-awareness and Self-reflection	fostered
	Self-direction and Self-management	fostered

401-5620-00L

Research Seminar on Statistics

0 credits

P. L. Bühlmann, N. Meinshausen, S. van de Geer, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur, J. Peters, M. Wolf

Abstract

Research colloquium

Learning objective

401-5640-00L

ZüKoSt: Seminar on Applied Statistics

0 credits

M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl, S. van de Geer

Abstract

5 to 6 talks on applied statistics.

Learning objective

Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.

Content

In 5-6 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen.
3 bis 4 der Vorträge stehen in der Regel unter einem Semesterthema.

Lecture notes

Bei manchen Vorträgen werden Unterlagen verteilt.
Eine Zusammenfassung ist kurz vor den Vorträgen im Internet unter http://stat.ethz.ch/talks/zukost abrufbar.
Ankündigunen der Vorträge werden auf Wunsch zugesandt.

Prerequisites / Notice

Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben.
Nach besonderem Programm. Koordinator M. Kalisch, Tel. 044 632 3435
Lehrsprache ist Englisch oder Deutsch je nach ReferentIn.
Course language is English or German and may depend on the speaker.