Advanced Visual Data Analysis, 6 credits (TNM098)

Avancerad visuell dataanalys, 6 hp

Main field of study

Media Technology and Engineering

Level

Second cycle

Course type

Programme course

Examiner

Matthew Cooper

Director of studies or equivalent

Camilla Forsell

Available for exchange students

Yes
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

A1X

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.

Prerequisites

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.

Examination

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

Grades

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

Department

Institutionen för teknik och naturvetenskap

Director of Studies or equivalent

Camilla Forsell

Examiner

Matthew Cooper

Education components

Preliminär schemalagd tid: 48 h
Rekommenderad självstudietid: 112 h
There is no course literature available for this course.
LAB1 Laboratory work U, G 1 credits
PRA1 Project assignment U, 3, 4, 5 5 credits

Regulations (apply to LiU in its entirety)

The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva. 

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