Advanced Visual Data Analysis, 6 credits (TNM098)

Avancerad visuell dataanalys, 6 hp

Main field of study

Media Technology and Engineering


Second cycle

Course type

Programme course


Matthew Cooper

Director of studies or equivalent

Camilla Forsell

Available for exchange students

Course offered for Semester Period Timetable module Language Campus VOF
6CMEN Media Technology and Engineering, M Sc in Engineering 8 (Spring 2017) 2 4 English Norrköping v
6MDAV Computer Science, Master's programme 2 (Spring 2017) 2 4 English Norrköping v
6MICS Computer Science, Master's programme 2 (Spring 2017) 2 4 English Norrköping v

Main field of study

Media Technology and Engineering

Course level

Second cycle

Advancement level


Course offered for

  • Media Technology and Engineering, M Sc in Engineering
  • Computer Science, Master's programme

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.


Skills in programming and computer graphics programming, mathematics, course Information Visualization or equivalent.


Intended learning outcomes

After completing the course the student should be able to:

  •   Examine new, complex data sets and identify relevant features which might be extracted
  •   Select and apply advanced algorithmic methods for analysis of large complex data sets to determine valuable results
  •   Address issues with very large data sets and develop approaches to the 'big data' problem
  •   Display extracted relevant information from such data sets using standard visualization methods

Course content

This course builds upon the course Information Visualization, with a focus on the data modelling, mining and analysis techniques with are the foundation of modern visual data analysis methodology. Such methods are becoming very important as the scale of data available for analysis expands, leading to the so-called 'big data' problem affecting business, healthcare, government, science and industry.

Teaching and working methods

The course is composed of lectures, laboratory assignments, seminar sessions and a substantial project work.


PRA1Project assignmentU, 3, 4, 55 credits
LAB1Laboratory workU, G1 credits


Four-grade scale, LiU, U, 3, 4, 5


Institutionen för teknik och naturvetenskap

Director of Studies or equivalent

Camilla Forsell


Matthew Cooper

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h
There is no course literature available for this course.
PRA1 Project assignment U, 3, 4, 5 5 credits
LAB1 Laboratory work U, G 1 credits

Regulations (apply to LiU in its entirety)

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LiU’s rule book for education at first-cycle and second-cycle levels is available at 

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