Advanced Visual Data Analysis, 6 credits

Avancerad visuell dataanalys, 6 hp

TNM098

Course starting semester

Main field of study

Media Technology and Engineering

Course level

Second cycle

Course type

Programme course

Examiner

Matthew Cooper

Director of studies or equivalent

Camilla Forsell

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Available for exchange students

Yes

Possible deviation from course syllabus/programme syllabus

ECV = Elective / Compulsory / Voluntary

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

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

PRA1	Project assignment	5 credits	U, 3, 4, 5
LAB1	Laboratory work	1 credits	U, G

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

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Code	Name	Scope	Grading scale
PRA1	Project assignment	5 credits	U, 3, 4, 5
LAB1	Laboratory work	1 credits	U, G

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.

Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)

Course literature is preliminary.

There is no course literature available for this course in studieinfo.

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize

		I	U	A	Modules
1. DISCIPLINARY KNOWLEDGE AND REASONING
	1.1 Knowledge of underlying mathematics and science (G1X level)
	1.2 Fundamental engineering knowledge (G1X level)			X	LAB1 PRA1
	1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)	X	X	X	LAB1 PRA1
	1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)
	1.5 Insight into current research and development work
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
	2.1 Analytical reasoning and problem solving	X	X	X	LAB1 PRA1
	2.2 Experimentation, investigation, and knowledge discovery	X	X	X	LAB1 PRA1
	2.3 System thinking	X	X		PRA1
	2.4 Attitudes, thought, and learning		X	X	LAB1 PRA1
	2.5 Ethics, equity, and other responsibilities
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
	3.1 Teamwork			X	PRA1
	3.2 Communications			X	PRA1
	3.3 Communication in foreign languages			X	PRA1
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
	4.1 External, societal, and environmental context			X	PRA1
	4.2 Enterprise and business context			X	PRA1
	4.3 Conceiving, system engineering and management		X	X	PRA1
	4.4 Designing	X	X	X	PRA1
	4.5 Implementing
	4.6 Operating
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
	5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development
	5.2 Economic conditions for knowledge development
	5.3 Identification of needs, structuring and planning of research or development projects
	5.4 Execution of research or development projects
	5.5 Presentation and evaluation of research or development projects

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