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SF2526 Numerical algorithms for data-intensive science 7.5 credits

Due to a great and increasing importance in relevance of large-scale data in various fields in science and technology, there is a need to understand the computational approaches used to analyze, understand and extract information from large amounts of data. The course gives an introduction to the use of many efficient numerical algorithms associated arising in problems in the analysis of large amounts of data. We use mathematical and numerical tools to study problems and algorithms.

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For course offering

Spring 2025 Start 14 Jan 2025 programme students

Application code


Headings with content from the Course syllabus SF2526 (Autumn 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course is mainly focused on the algorithmic and practical computational aspects and applications in the following topics:

  • Numerical algorithms for data-intensive least squares problems
  • Numerical algorithms for large graphs, networks and clustering
  • Numerical algorithms for distance measures and classification

Intended learning outcomes

The general intended objective is to obtain understanding when the algorithms of the course work well and their implementation, justification and analysis. More specifically, after a completed course the student shall be able to

  • implement linear algebra algorithms for topics of the blocks of the course;
  • analyze when the algorithms of the course work well and their limitations, by using linear algebra tools;
  • justify or derive methods of the course, using mathematical reasoning and relation to other numerical techniques;
  • apply the methods of the course to solve scientific problems relevant for a sustainable society

Literature and preparations

Specific prerequisites

  • Completed basic course in numerical analysis (SF1544, SF1545or equivalent) and
  • Completed basic course in computer science (DD1320 or equivalent).

Recommended prerequisites

SF2520 Applied Numerical Methods (or equivalent), can be read in parallel.


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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


  • LAB1 - Laboratory work, 3.5 credits, grading scale: P, F
  • TEN1 - Exam, 4.0 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

Second cycle

Add-on studies

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Elias Jarlebring (