401-3632-00L Computational Statistics

Number

Title

Hours

Lecturers

401-3632-00 V

Computational Statistics

3 hrs

401-3632-00 U

Computational Statistics

A "Präsenzstunde" directly following the exercises will be offered Friday 11-12 in HG G 5.

1 hrs

Fri	10:00-11:00	ER SA TZ »
	10:15-11:00	HG G 5 »

Abstract	We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.
Learning objective	The student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R.
Content	See the class website
Prerequisites / Notice	At least one semester of (basic) probability and statistics. Programming experience is helpful but not required.

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	8 credits
Examiners	M. H. Maathuis
Type	session examination
Language of examination	English
Repetition	The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination	written 180 minutes
Additional information on mode of examination	This is a computer exam. Some of the questions require the use of the statistical programming language R.
Written aids	One sheet of paper (A4, front and back) with a machine- or handwritten summary.
Digital exam	The exam takes place on devices provided by ETH Zurich.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

No public learning materials available.
Only public learning materials are listed.

No information on groups available.

There are no additional restrictions for the registration.

Programme	Section	Type
CAS in Computer Science	Focus Courses and Electives	W
Computational Biology and Bioinformatics Master	Theory	W
Cyber Security Master	Core Courses	W
DAS in Data Science	Foundations Courses	W
Data Science Master	Statistics	W
Computer Science Master	Focus Core Courses Computational Science	W
Computer Science Master	Core Focus Courses General Studies	W
Mathematics Bachelor	Core Courses: Applied Mathematics and Further Appl.-Oriented Fields	W
Mathematics Master	Core Courses: Applied Mathematics and Further Appl.-Oriented Fields	W
Micro- and Nanosystems Master	Modelling and Simulation	W
Computational Science and Engineering Master	Core Courses	W
Statistics Master	Statistical and Mathematical Courses	W