VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Computer Science II
Informatik II
Last Updated: 2026-02-05 16:38:16
Abstract
This course provides the foundations of programming and working with data. Computer Science II particularly stresses code efficiency and provides the basis for understanding, design, and analysis of algorithms and data structures.
Objective
Based on the knowledge covered by the lecture Computer Science I, the primary educational objective of this course is the constructive knowledge of data structures and algorithms. After successfully attending the course, students have a good command of the mechanisms to construct a program in Python and to work with multidimensional data using Python libraries. Students particularly understand how an algorithmic problem can be solved with a sufficiently efficient computer program. Secondary educational objectives are formal thinking, the power of abstraction, and appropriate modeling capabilities. In the course "Computer Science II", the competencies of programming, modeling and data analysis & interpretation are taught, applied and examined.
Content
Introduction of Python: from Java to Python, advanced concepts and built-in data structures in Python; parsing data, operating on data using Numpy and visualization using Matplotlib; mathematical tools for the analysis of algorithms (asymptotic function growth, recurrence equations, recurrence trees), classical algorithmic problems (searching, selection and sorting), design paradigms for the development of algorithms (divide and conquer and dynamic programming), data structures for different purposes (linked lists, trees, heaps, hash tables). The relationship and tight coupling between algorithms and data structures are illustrated with graph algorithms (traversals, topological sort, shortest paths, minimum spanning tree, maximum flow) and geometric algorithms (scanline). In general, the concepts provided in the course are motivated and illustrated with practically relevant algorithms and applications. Exercises are carried out in Code Expert, an online IDE and exercise management system. The programming language used in this course is Python.
Resources
Lecture Notes
The slides will be made available for download on the course website.
Literature
T. Cormen, C. Leiserson, R. Rivest, C. Stein, Introduction to Algorithms , 3rd ed., MIT Press, 2009
Learning Materials (Links)
- Main link
- Vorlesungshomepage Informatik II D-BAUG
General Information
- Language
- German
- Levels
- BSC , DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes and 120 minutes
- Aids
- Informatik I und II jeweils: maximal 4 A4-Blätter. Inhaltliche und formale Anforderungen (Text, Bilder, ein-/doppelseitig, Ränder, Schriftgrösse, etc.) bestehen nicht. Elektronische Geräte bzw. digitale Unterlagen sind nicht erlaubt.
- Digital
- The exam takes place on devices provided by ETH Zurich.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Informatik II |
|
2 h weekly |
| exercise |
Informatik II
Zusätzlich wird ab der zweiten Semesterwoche ein Study Center angeboten: Montags von 10-12 in CHN D29.
|
|
2 h weekly |
Offered In
-
-
-
-
Geospatial Engineering Bachelor (Registration via myStudies for a thesis during spring semester until 15 Januaryt at the latest, for a thesis during autumn semester until 15 August at the latest.)
-