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Course syllabus - Data Management and Datafication

Scope

7.5 credits

Course code

DVA256

Valid from

Autumn semester 2026

Education level

First cycle

Progressive Specialisation

G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements)

Main area(s)

Computer Science

Organisation

Department of Computer Science & Engineering

Ratified

2020-12-15

Revised

2025-11-03

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2022-01-26
    • Latest update: 2025-05-27

    Other Materials

    Handout notes and list of literature will be provided during the course.

Objectives

The course aims to give an introduction to the theoretical foundation of data management, modelling and datafication. It covers the key concepts and insights about data collection, data manipulation, value creation and extraction of knowledge from the data for making meaningful decisions.

Learning outcomes

After completing the course the student shall be able to:

  1. describe the basics of data, data collection,data documentation and ethical issues in data handling,
  2. describe the principle of data modelling,
  3. demonstrate the ability to describe concepts of datafication, and its consequences in meaning-making process
  4. demonstrate the ability to explain the challenges associated with structured and unstructured data and
  5. demonstrate the ability to design and develop solutions to create value from data.

Course content

  • Data and data types, data collection techniques and different standards to be used for data and metadata format,
  • Documentation and data management approaches (e.g., plans for archiving data and samples) including ethical issues,
  • Concepts of data modelling e.g., relational data model and others,
  • Methodology and technology to explore datafication i.e., conversion of qualitative aspects of data into quantified data,
  • Different Information retrieval algorithms including statistical and probabilistic information retrieval
  • Data transformation methods such as signal processing algorithms e.g., Fourier transform, concept of information entropy and dealing with high dimensional data.

Specific requirements

Programming 7,5 credits, Data structures, algorithms and program design 7,5 credits or equivalent and Vector algebra 7,5 credits, or equivalent.

Examination

Exercise (OVN1), 3 hp, examines the learning outcomes 3-5, marks Fail (U), 3, 4, or 5.
Written assignment (INL1) 3 hp, examines the learning outcomes 1-5, marks Fail (U), 3, 4, or 5.
Seminar (SEM1), 1,5 hp, examines the learning outcomes 1-5, marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Grading scale: 5, 4, 3