## 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 |

faessler@inf.ethz.ch | |

URL | https://et.ethz.ch/ |

Department | Computer Science |

Relationship | Lecturer |

Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||
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252-0839-00L | Informatics | 2 credits | 2G | L. E. Fässler, M. Dahinden | |||||||||||||||||||||||||||||||||||

Abstract | Students 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. | ||||||||||||||||||||||||||||||||||||||

Objective | The 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. | ||||||||||||||||||||||||||||||||||||||

Content | 1. 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 notes | All materials for the lecture are available at www.evim.ethz.ch | ||||||||||||||||||||||||||||||||||||||

Prerequisites / Notice | This 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. | ||||||||||||||||||||||||||||||||||||||

252-0852-00L | Foundations of Computer Science | 4 credits | 2V + 2U | L. E. Fässler, M. Dahinden | |||||||||||||||||||||||||||||||||||

Abstract | Students 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. | ||||||||||||||||||||||||||||||||||||||

Objective | The 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. | ||||||||||||||||||||||||||||||||||||||

Content | 1. 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 notes | All materials for the lecture are available at www.gdi.ethz.ch | ||||||||||||||||||||||||||||||||||||||

Literature | L. 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 / Notice | This 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. | ||||||||||||||||||||||||||||||||||||||

Competencies |
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265-0100-00L | Foundations of Programming Only for CAS in Applied Information Technology and MAS in Applied Technology. | 3 credits | 2A | L. E. Fässler | |||||||||||||||||||||||||||||||||||

Abstract | The 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. | ||||||||||||||||||||||||||||||||||||||

Objective | Students 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. | ||||||||||||||||||||||||||||||||||||||

Content | The 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 / Notice | No 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. |