Algorithms and Data Structures I

5 credits

Course, Bachelor's level, 1DL210

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
1 September 2025–2 November 2025
Language of instruction
English
Entry requirements

10 credits in computer programming (Program Design, Programming Techniques II, or the equivalent) and 10 credits in mathematics, including basic algebra. Alternatively 45 credits in the Master's Programme in Language Technology (HSP2M).

Selection

Higher education credits in science and engineering (maximum 240 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 10,833
  • Total tuition fee: SEK 10,833

Read more about fees.

Application deadline
15 April 2025
Application code
UU-11032

Admitted or on the waiting list?

Registration period
25 July 2025–7 September 2025
Information on registration from the department

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
1 September 2025–2 November 2025
Language of instruction
English
Entry requirements

10 credits in computer programming (Program Design, Programming Techniques II, or the equivalent) and 10 credits in mathematics, including basic algebra. Alternatively 45 credits in the Master's Programme in Language Technology (HSP2M).

Admitted or on the waiting list?

Registration period
25 July 2025–7 September 2025
Information on registration from the department

About the course

The course treats a number of fundamental data structures such as linked lists, stacks, queues, and trees, as well as more advanced data structures. These structures can be used to implement sequences, sets and graphs - where different choices have different effects on the usage of memory and time. An important part of the course is the mathematical analysis of the execution time as a function of the size of the problem. The course treats also general principles for the construction of algorithms, as well as questions like "What is theoretically the fastest possible way to solve this problem?".

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