Algorithms and Data Structures II
Course, Bachelor's level, 1DL231
Expand the information below to show details on how to apply and entry requirements.
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 November 2026–17 January 2027
- Language of instruction
- English
- Entry requirements
-
60 credits of which 15 credits in mathematics and 25 credits in computer science. Alternatively 45 credits in the Master's Programme in Language Technology. Participation in Program Design and Data Structures of which 9 credits shall be completed, alternatively participation in Algorithms and Data Structures I. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 April 2026
- Application code
- UU-11016
Admitted or on the waiting list?
- Registration period
- 19 October 2026–8 November 2026
- Information on registration from the department
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 November 2026–17 January 2027
- Language of instruction
- English
- Entry requirements
-
60 credits of which 15 credits in mathematics and 25 credits in computer science. Alternatively 45 credits in the Master's Programme in Language Technology. Participation in Program Design and Data Structures of which 9 credits shall be completed, alternatively participation in Algorithms and Data Structures I. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 19 October 2026–8 November 2026
- Information on registration from the department
About the course
The course extends and deepens your knowledge about algorithms and their analysis. After the course, you can design algorithms based on techniques such as dynamic programming and greedy algorithms, as well as derive their complexity from recursive equations. List of topics:
- solving recursive equations that describe the algorithm's complexity
- dynamic programming
- greedy algorithms
- graph algorithms: minimum spanning trees, shortest paths
- string matching.
Reading list
- Reading list valid from Autumn 2022, version 2
- Reading list valid from Autumn 2022, version 1
- Reading list valid from Autumn 2019
- Reading list valid from Autumn 2017
- Reading list valid from Spring 2013
- Reading list valid from Autumn 2012, version 3
- Reading list valid from Autumn 2012, version 2
- Reading list valid from Autumn 2012, version 1