Lukas Emanuel Fässler: Catalogue data in Autumn Semester 2021

Name Dr. Lukas Emanuel Fässler
Address
Dep. Informatik
ETH Zürich, CAB H 32.1
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telephone+41 44 632 09 16
E-mailfaessler@inf.ethz.ch
URLhttps://et.ethz.ch/
DepartmentComputer Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
252-0839-00LInformatics Information 2 credits2GL. E. Fässler, M. Dahinden
AbstractStudents learn to apply selected concepts and tools from computer science for working on interdisciplinary projects. The following topics are covered: modeling and simulations, managing data with lists and tables and with relational databases, introduction to programming.
ObjectiveThe students learn to

- choose and apply appropriate tools from computer science,
- process and analyze real-world data from their subject of study,
- handle the complexity of real-world data.
Content1. Modeling and simulations
2. Data management with lists and tables
3. Data management with a relational database
4. Introduction to macro programming
5. Introduction to programming with Python
Lecture notesAll materials for the lecture are available at www.evim.ethz.ch
Prerequisites / NoticeThis course is based on application-oriented learning. The students spend most of their time working through projects with data from natural science and discussing their results with teaching assistants. To learn the computer science basics there are electronic tutorials available.
252-0852-00LFoundations of Computer Science Information 4 credits2V + 2UL. E. Fässler, M. Dahinden
AbstractStudents learn to apply selected concepts and tools from computer science for working on interdisciplinary projects.

The following topics are covered: modeling and simulations, introduction to programming, introduction matrices, managing data with lists and tables and with relational databases, universal methods for algorithm design.
ObjectiveThe students learn to

- understand the role of computer science in science,
- to control computer and automate processes of problem solving by programming,
- choose and apply appropriate tools from computer science,
- process and analyze real-world data from their subject of study,
- handle the complexity of real-world data.
Content1. The role of computer science in science
2. Introduction to Programming with Python
3. Modeling and simulations
4. Data management with lists and tables
5. Data management with a relational database
6. Introduction to Matrices
Lecture notesAll materials for the lecture are available at www.gdi.ethz.ch
LiteratureL. Fässler, M. Dahinden, D. Komm, and D. Sichau: Einführung in die Programmierung mit Python und Matlab. Begleitunterlagen zum Onlinekurs und zur Vorlesung, 2016. ISBN: 978-3741250842.
L. Fässler, M. Dahinden, and D. Sichau: Verwaltung und Analyse digitaler Daten in der Wissenschaft. Begleitunterlagen zum Onlinekurs und zur Vorlesung, 2017.
Prerequisites / NoticeThis course is based on application-oriented learning. The students spend most of their time working through projects with data from natural science and discussing their results with teaching assistants. To learn the computer science basics there are electronic tutorials available.
CompetenciesCompetencies
Subject-specific CompetenciesTechniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesfostered
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingassessed
Social CompetenciesCommunicationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
265-0100-00LFoundations of Programming Restricted registration - show details
Only for CAS in Applied Information Technology and MAS in Applied Technology.
3 credits2AL. E. Fässler
AbstractThe initial module offers a practical introduction to some basic concepts and techniques for information processing as well as practical applications of them. The programming language are Python and SQL.
ObjectiveStudents learn...

- how to encode a problem into a program, test the program, and correct errors.
- to understand and improve existing code.
- to implement mathematical models as a simulation.
ContentThe following programming concepts are introduced during this module:

1. Variables, data types
2. Condition check, Loops, logics
3. Arrays
4. Functions
5. Matrices
6. Data management (SQL)

In the practical part of the course, students work on small programming projects with a context from natural sciences. Electronic tutorials are available as preparation.
Prerequisites / NoticeNo prior knowledge is required for this course. It is based on application-oriented learning. The students spend most of their time working through programming projects and discussing their results with teaching assistants. To learn the programming basics there are electronic tutorials available.