DS800: Introduction to Data Processing

Study Board of Science

Teaching language: Danish or English depending on the teacher
EKA: N340041102
Censorship: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340041101
ECTS value: 10

Date of Approval: 27-02-2019

Duration: 1 semester

Version: Archive


New course autumn 2019
Kurset kan ikke følges af studerende, der: har fulgt eller bestået DM561, eller DM562, eller et kursus med lignende indhold

Entry requirements

The course cannot be taken by students enrolled in the master programme in Computer Science.

Academic preconditions


Course introduction

The aim of the course is to enable the student to represent and describe data and to write small computer programs to read, collect, integrate, clean, validate, and prepare data for scientific computations. This is important in regard to the rest of education in Data Science as it provides the basis for carrying out data analysis projects. 

The course will give to the student knowledge and competence in methods from linear algebra, such as matrices and matrix calculations, which allow a mathematical description of a data science task. In addition, the course will provide skills in writing small computer programs to carry out scientific computations of the type encountered in linear algebra or of the type that will be needed later in the education.  

The course gives an academic basis for studying the topics Applied Statistics, Multivariate Analysis, Datamining and Machine Learning, Applied Machine Learning, Visualization and Deep Learning, that are part of the degree.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • Give the competence to handle, analyse and present data
  • Give knowledge and understanding of programming
  • Give the competence to design, select, apply and integrate the right programming tools to process and analyze large amounts of data as well as make calculations with them
  • Give the competence to use and further develop existing programming tools to perform complex data analyzes and work with advanced data
  • Give skills in software development 
  • Give skills in data collection, cleaning, validation, integration and preparation 
  • Give knowledge and understanding of methods for working with larger amounts of data in general and within a given subject area.
  • Give knowledge and understanding of theories at the basis of data science methods

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:
  • Use data representations from linear algebra to describe and report data analyses
  • Recognize which methods from linear algebra can be used for different tasks in data analysis
  • Develop small computer programs (scripts) in an appropriate programming language to process data
  • Select and use programming tools to collect, clean and prepare data
  • Apply linear algebra methods to extract knowledge from data


The following main topics are contained in the course:

Linear Algebra:
  • sets and functions
  • vector spaces
  • linear functions
  • matrix operations
  • determinants
  • linear equation systems
  • eigenvalues and eigenvectors
  • principle component analysis
  • basics
  • values and data types
  • control flow (choice, loops)
  • data structures (lists, associative)
  • functions, classes
  • file I/O, exceptions
  • basic data visualization
Practical Applications on Linear Algebra & Programming


See Blackboard for syllabus lists and additional literature references.

Examination regulations

Exam element a)




The exam consists of a number of practical assignments submitted during the course and a written examination.




Second examiner: External


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

Allowed, a closer description of the exam rules will be posted under 'Course Information' on Blackboard.

ECTS value


Additional information

The written exam takes place in January.
Eksamensformen ved reeksamen kan være en anden end eksamensformen ved den ordinære eksamen. 

Indicative number of lessons

90 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

The intro phase facilitates the introduction to new material and topics, which in the skills training phase are processed with exercises prepared at home and discussed in class to validate the acquired knowledge. The study activity in form of practical applications gives the students the possibility to apply and use the knowledge acquired.

Study phase activities:
  • Reading from text books
  • Solving homeworks 
  • Applying acquired knowledge to practical projects

Teacher responsible

Name E-mail Department
John Bulava bulava@imada.sdu.dk Institut for Matematik og Datalogi, Anvendt Matematik
Stefan Jänicke stjaenicke@imada.sdu.dk Institut for Matematik og Datalogi, Datalogi


Administrative Unit

Institut for Matematik og Datalogi (datalogi, fiktiv)

Team at Registration & Legality


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