SF2526 Numerical algorithms for data-intensive science 7.5 credits

Numeriska algoritmer för vetenskapliga problem med stora datamängder

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.

  • Education cycle

    Second cycle
  • Main field of study

  • Grading scale

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

Course offerings

Spring 19 for programme students

  • Periods

    Spring 19 P3 (7.5 credits)

  • Application code


  • Start date


  • End date


  • Language of instruction


  • Campus

    KTH Campus

  • Tutoring time


  • Form of study


  • Number of places

    No limitation

  • Schedule

    Schedule (new window)

  • Planned timeslots

    P3: E1, A2. more info

  • Course responsible

    Elias Jarlebring <eliasj@kth.se>

  • Teacher

    Elias Jarlebring <eliasj@kth.se>

Spring 20 for programme students

Intended learning outcomes

After completing the course, the student will be familiar with important numerical methods and algorithms used to analyze data and describe when they are useful.

  • The student should be able to independently identify and formulate the problems data-oriented problem classes presented in the course.
  • The student will be select an appropriate algorithm to use to the solve the problems.
  • The student should be able to describe algorithm properties and associate them with specific problem properties
  • The student will be able to derive new variants and methods based generalizing methods in the course.

Course main content

The course is mainly focused on the algorithmic and practical computational aspects in the following blocks.

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


  • Lectures 
  • Laborations 


Basic course in numerical analysis and computer science. 

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology.  English B, or equivalent


The course literature will be announced in the detailed course syllabus.


  • LAB1 - Laboratory work, 3.5, grading scale: P, F
  • TEN1 - Exam, 4.0, grading scale: A, B, C, D, E, FX, F

The examiner decides, in consultation with KTHs Coordinator of students with disabilities (Funka), about any customized examination for students with documented,lastingdisability. The examiner may allow another form of examination for reexamination of individual students.

Requirements for final grade

  • Laborations completed (LABA)
  • Written Exam completed (TEN1)

Offered by



Elias Jarlebring (eliasj@kth.se)


Elias Jarlebring <eliasj@kth.se>


Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.