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DD1328 Fundamentals of Computer Science for Scientific Computing 9.0 credits

A basic course in computer science and parallel programming, for students from the degree programme in engineering mathematics.

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Spring 2025 grudatt25 programme students

Application code


Headings with content from the Course syllabus DD1328 (Spring 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

In this course, the students will build on their knowledge of algorithms, data structures and program design, learn basics of parallel and distributed programming and prepare for courses in computational mathematics, machine learning and theoretical computer science. Programming languages in the course are Python and Go, and the students who want can furthermore test on one or more of the languages Java, Julia, C, and C++.

Algorithms and data structures: A systematic overview of the concepts abstract data types, stacks, queues, lists, trees, searching, sorting and recursion based on the knowledge the students acquired in the course Fundamentals of programming. Hashing. Priority queues. Search trees. Problem trees. Text searching. Simple syntactical analysis. Algorithm analysis. Cryptography.

Program Design: Program quality. Abstraction. Modularisation. Testing. System calls. Standard modules.

Parallel Programming: Basic knowledge of how a computer is working and what it is made up of, both from hardware and software perspectives. Introduction to parallel and distributed programming with processes

Intended learning outcomes

After passing the course, the student should be able to

  • systematically test programs to discover errors
  • use abstraction as a tool to simplify the programming
  • select an appropriate algorithm for a given problem
  • compare algorithms with regard to time and memory usage
  • describe and implement different algorithms for search and sorting
  • formulate and implement recursive algorithms
  • write and use simple BNF syntax
  • implement, and design algorithms for, basic data structures
  • design and implement simple parallel programs

in order to

  • become a good problem solver using programming
  • be able to use computational methods in application projects
  • take advanced courses in computational mathematics, machine learning and theoretical computer science.

Literature and preparations

Specific prerequisites

Knowledge and skills in programming, 5 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course.

Being registered for a course counts as active participation.

The term 'final examination' encompasses both the regular examination and the first re-examination.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • HEM3 - Take-home Assignment, 3.0 credits, grading scale: P, F
  • LABA - Programming Assignment, 2.0 credits, grading scale: A, B, C, D, E, FX, F
  • LABB - Programming Assignment, 2.0 credits, grading scale: A, B, C, D, E, FX, F
  • LABC - Programming Assignment, 2.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study


Education cycle

First cycle

Add-on studies

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Hedvig Kjellström (

Supplementary information

In this course, the EECS code of honor applies, see: